The recent increase in the availability of spaceborne radar in different wavelengths with multiple polarisations provides new opportunities for land surface analysis. This research effort explored how different radar data, and derived texture values, indepen- dently and in combination with optical imagery influence land cover/use classification accuracies for a study site in Washington, DC, USA. Two spaceborne radar images, Radarsat-2L-band and Palsar C-band quad-polarised radar, were registered with Aster optical data for this study. Traditional methods of classification were applied to various components and combinations of this data set, and overall and class-specific thematic accuracies obtained for comparison. The results for the two despeckled radar data sets were quite different, with Radarsat-2 obtaining an overall accuracy of 59% and Palsar 77%, while that of the optical Aster was 90%. Combining the original radar and a variance texture measure increased the accuracy of Radarsat-2 to 71% but that of Palsar only to 78%. One of the sensor fusions of optical and radar obtained an accuracy of 93%. For this location, radar by itself does not obtain classification accuracies as high as optical data, but fusion with optical imagery provides better overall thematic accuracy than the optical independently, and results in some useful improvements on a class-by-class basis. For those regions with high cloud cover, quad polarisation radar can independently provide viable results but it may be wavelength-dependent.
An evaluation of Radarsat-2 individual and combined image dates for land use/...rsmahabir
Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarized images were obtained for Washington, D.C., USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73% and 82%.
Relative value of radar and optical data for land cover/use mapping: Peru exa...rsmahabir
This study determined using divergence measures the best indivi- dual and combinations of various numbers of bands for six land cover/use classes around the city of Arequipa, Peru. A 15 band data stack consisting of PALSAR L-band dual-polarised radar, Landsat optical data, as well as six variance texture measures extracted from the PALSAR images, was used in this study. Spectral signatures were obtained for each class for the diver- gence examination. The band having the highest separability was the Landsat visible red band followed by the two largest window PALSAR texture measures. The best three band combina- tion included three very different data types, Landsat visible red, near infrared and the PALSAR HH variance texture from a 17 × 17 pixel window. There was no need based upon the diver- gence values to use more than five bands for classification.
Automatic traffic light controller for emergency vehicle using peripheral int...IJECEIAES
Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.
Radar speckle reduction and derived texture measures for land cover/use class...rsmahabir
This study examined the appropriateness of radar speckle reduction for deriving texture measures for land cover/use classifications. Radarsat-2 C-band quad-polarized data were obtained for Washington, D.C., USA. Polarization signatures were extracted for multiple image components, classified with a maximum-likelihood decision rule and thematic accuracies determined. Initial classifications using original and despeckled scenes showed despeckled radar to have better overall thematic accuracies. However, when variance texture measures were extracted for several window sizes from the original and despeckled imagery and classified, the accuracy for the radar data was decreased when despeckled prior to texture extraction. The highest classification accuracy obtained for the extracted variance texture measure from the original radar was 72%, which was reduced to 69% when this measure was extracted from a 5x5 despeckled image. These results suggest that it may be better to use despeckled radar as original data and extract texture measures from the original imagery.
An evaluation of Radarsat-2 individual and combined image dates for land use/...rsmahabir
Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarized images were obtained for Washington, D.C., USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73% and 82%.
Relative value of radar and optical data for land cover/use mapping: Peru exa...rsmahabir
This study determined using divergence measures the best indivi- dual and combinations of various numbers of bands for six land cover/use classes around the city of Arequipa, Peru. A 15 band data stack consisting of PALSAR L-band dual-polarised radar, Landsat optical data, as well as six variance texture measures extracted from the PALSAR images, was used in this study. Spectral signatures were obtained for each class for the diver- gence examination. The band having the highest separability was the Landsat visible red band followed by the two largest window PALSAR texture measures. The best three band combina- tion included three very different data types, Landsat visible red, near infrared and the PALSAR HH variance texture from a 17 × 17 pixel window. There was no need based upon the diver- gence values to use more than five bands for classification.
Automatic traffic light controller for emergency vehicle using peripheral int...IJECEIAES
Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.
Radar speckle reduction and derived texture measures for land cover/use class...rsmahabir
This study examined the appropriateness of radar speckle reduction for deriving texture measures for land cover/use classifications. Radarsat-2 C-band quad-polarized data were obtained for Washington, D.C., USA. Polarization signatures were extracted for multiple image components, classified with a maximum-likelihood decision rule and thematic accuracies determined. Initial classifications using original and despeckled scenes showed despeckled radar to have better overall thematic accuracies. However, when variance texture measures were extracted for several window sizes from the original and despeckled imagery and classified, the accuracy for the radar data was decreased when despeckled prior to texture extraction. The highest classification accuracy obtained for the extracted variance texture measure from the original radar was 72%, which was reduced to 69% when this measure was extracted from a 5x5 despeckled image. These results suggest that it may be better to use despeckled radar as original data and extract texture measures from the original imagery.
During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased
to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time
slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing
maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural
networks have been successfully used to extract features of geologic interest from multiple volumes. Although
well documented in the literature, the terminology and complexity of these algorithms may bewilder the average
seismic interpreter, and few papers have applied these competing methods to the same data volume. We have
reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the
Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural
elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of
the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found
that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised
learning methods also highlighted features that might otherwise be overlooked.
study and analysis of hy si data in 400 to 500IJAEMSJORNAL
The ability to extract information about world and present it in way that our visual perception can comprehend is ultimate goal of imaging science in remote sensing .Hyperspectral imaging system is most powerful tool in the field of remote sensing also called as imaging spectroscopy, It is new technique used by researcher to detect terrestrial, vegetation and mineral. This paper reports analysis of hyperspectral images. Firstly the hyperspectral image analyzed by using supervised classification of Amravati region from Maharashtra province of India. The report reveals spectral analysis of Amravati region. We acquired satellite imagery to perform the classification using maximum like hood classifier. Analysis is performing in ERDAS to determine the spectral reflectance against the no of band. The analytical outcome of paper is representing the soil, water, vegetation index of the region.
Determine the amount of human body components fat by x-ray spectral information using MARS spectral X-ray scanner and also, study of the x-ray spectral information.
Cancerous lung nodule detection in computed tomography imagesTELKOMNIKA JOURNAL
Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Comparison and integration of spaceborne optical and radar data for mapping i...rsmahabir
The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using sepa- rate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or inte- grated with optical imagery has great potential in improving the accuracy of land- cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan ...rsmahabir
Abstract-The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased
to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time
slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing
maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural
networks have been successfully used to extract features of geologic interest from multiple volumes. Although
well documented in the literature, the terminology and complexity of these algorithms may bewilder the average
seismic interpreter, and few papers have applied these competing methods to the same data volume. We have
reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the
Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural
elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of
the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found
that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised
learning methods also highlighted features that might otherwise be overlooked.
study and analysis of hy si data in 400 to 500IJAEMSJORNAL
The ability to extract information about world and present it in way that our visual perception can comprehend is ultimate goal of imaging science in remote sensing .Hyperspectral imaging system is most powerful tool in the field of remote sensing also called as imaging spectroscopy, It is new technique used by researcher to detect terrestrial, vegetation and mineral. This paper reports analysis of hyperspectral images. Firstly the hyperspectral image analyzed by using supervised classification of Amravati region from Maharashtra province of India. The report reveals spectral analysis of Amravati region. We acquired satellite imagery to perform the classification using maximum like hood classifier. Analysis is performing in ERDAS to determine the spectral reflectance against the no of band. The analytical outcome of paper is representing the soil, water, vegetation index of the region.
Determine the amount of human body components fat by x-ray spectral information using MARS spectral X-ray scanner and also, study of the x-ray spectral information.
Cancerous lung nodule detection in computed tomography imagesTELKOMNIKA JOURNAL
Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Comparison and integration of spaceborne optical and radar data for mapping i...rsmahabir
The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using sepa- rate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or inte- grated with optical imagery has great potential in improving the accuracy of land- cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.
Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan ...rsmahabir
Abstract-The purpose of this study was to determine via spectral separability using divergence measures the best individual and combinations of various numbers of bands for five land cover/ land use classes along the Blue Nile in Sudan. The data for this analysis were a stack of 15 layers including RADARSAT-2 C-band and PALSAR L-band quad-polarized radar registered with ASTER optical data, as well as four variance texture measures extracted from the RADARSAT-2 images. Spectral signatures were obtained for each class and examined by various separability measures. This examination is useful for better understanding the relative value of different types of remote sensing data and best band combinations for possible visual analysis and for improving land cover/ land use classification accuracy. Results show that the best single band for analysis was the RADARSAT-2 VH variance texture measure. The best pair of bands was the ASTER visible red and the RADARSAT-2 HV variance texture, which also included the PALSAR VH band for the best three band combination, all bands being very different data types. Further, based upon the divergence values, only eight bands are needed to achieve maximum separation between land cover/ land use classes. Beyond this point, classification accuracy is expected to decrease, with as few as six bands needed to reach viable classification accuracy.
Performance analysis of change detection techniques for land use land coverIJECEIAES
Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods.
Remote sensing-derived national land cover land use maps: a comparison for Ma...rsmahabir
Reliable land cover land use (LCLU) information, and change over time, is impor- tant for Green House Gas (GHG) reporting for climate change documentation. Four different organizations have independently created LCLU maps from 2010 satellite imagery for Malawi for GHG reporting. This analysis compares the procedures and results for those four activities. Four different classification methods were employed; traditional visual interpretation, segmentation and visual labelling, digital clustering with visual identification and supervised signature extraction with application of a decision rule followed by analyst editing. One effort did not report classification accuracy and the other three had very similar and excellent overall thematic accura- cies ranging from 85 to 89%. However, despite these high thematic accuracies there were very significant differences in results. National percentages for forest ranged from 18.2 to 28.7% and cropland from 40.5 to 53.7%. These significant differences are concerns for both remote-sensing scientists and decision-makers in Malawi.
Flood Detection Using Empirical Bayesian NetworksIOSRJECE
Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Flood is not only one of the widest spread natural disasters, which regularly causes large numbers of casualties with rising economic loss, extensive homelessness and disaster induced disease, but is also the most frequent disaster type. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this work, an Empirical Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information where as in the previous work the authors has used the Bayesian networks. The methodology is tested on a case study regarding a flood that occurred in the Visakhapatnam (India) on October 2014, monitored using a time series of TerraSAR-X data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.
Similar to Radar and optical remote sensing data evaluation and fusion; a case study for Washington, DC, USA (20)
A Critical Review of High and Very High-Resolution Remote Sensing Approaches ...rsmahabir
Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High- and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.
Impact of road networks on the distribution of dengue fever cases in Trinidad...rsmahabir
This study examined the impact of road networks on the distribution of dengue fever cases in Trinidad, West Indies. All confirmed cases of dengue hemorrhagic fever (DHF) observed during 1998 were georef- erenced and spatially located on a road map of Trinidad using Geographic Information Systems software. A new digital geographic layer representing these cases was created and the distances from these cases to the nearest classified road category (5 classifications based on a functional utility system) were examined. The distance from each spatially located DHF case to the nearest road in each of the 5 road subsets was determined and then subjected to an ANOVA and t-test to determine levels of association between minor road networks (especially 3rd and 4th class roads) and DHF cases and found DHF cases were located away from forests, especially 5th class roads). The frequency of DHF cases to different road classes was: 0% (1st class roads), 7% (2nd class roads), 32% (3rd class roads), 57% (4th class roads) and 4% (5th class road). The data clearly demonstrated that both class 3 and class 4 roads account for 89% of nearby dengue cases. These results represent the first evidence of dengue cases being found restricted between forested areas and major highways and would be useful when planning and implementing control strategies for dengue and Aedes aegypti mosquitoes.
The Rabies Epidemic in Trinidad of 1923 to 1937: An Evaluation with a Geograp...rsmahabir
Background.—Rabies, although not preeminent among current infectious diseases, continues to afflict humans with as many as 55,000 deaths annually. The case fatality rate remains the highest among infectious diseases, and medical treatments have proven ineffective.
Objective.—This study analyzes the rabies epidemic of 1929 to 1937 in Trinidad from a geograph- ical perspective, using Geographic Information System (GIS) software as an analytical tool.
Setting.—A small island developing country at a time when infectious diseases were rampant.
Methods.—A review of the literature was undertaken, and data were collected on the occurrence of disease in both animal and humans populations and mapped using GIS software. Several factors identified in the literature were further explored such as land use/land cover, rainfall and magnetic declination.
Results.—The bat rabies epidemic of 1923 to 1937 in Trinidad was migratory and seasonal, shifting to new locations along a definite path. The pattern of spread appears to be spatially linked to land use/land cover. The epidemic continues to present many unexplained peculiarities.
Conclusion.—Despite the fact that this epidemic occurred almost 7 decades ago, the application of new tools available for public health use can create new knowledge and understanding of events. We showed that the spatial of distribution of the disease followed a distinct pathway possible due to the use of electromagnetic capabilities of bats.
The Role of Spatial Data Infrastructure in the Management of Land Degradation...rsmahabir
Abstract
Land degradation involves a wide array of natural and human induced factors affecting the productivity of land. These factors can exist in various non unique and complex combinations of different environmental settings, making detection and monitoring of land degradation an often difficult undertaking. As a result, no universal solution exists to eliminate the problem of land degradation altogether. In order to reduce its rate of encroachment, this phenomenon should be assessed and quantified in order to identify the causes, processes and factors leading to land degradation.
In small tropical and Caribbean islands, there exists a severe shortage of good, reliable and up- to-date information bases for the contributing factors of land degradation. In addition to the limited knowledge about what spatial datasets already exist, there is also no agreed minimum level of quality for datasets and metadata documentation standards. As a result, datasets produced to help in understanding and treating land degradation problems may have unknown or unacceptable levels of uncertainty. This may require re-development of already existing datasets, hence consuming further efforts, financial resources, and time. In critical circumstances where land degradation posses severe threat to the environment and therefore indirectly to humans, the incurred price of a slow or ill informed decision may eventually render the state of land unrecoverable.
It is postulated that Spatial Data Infrastructure (SDI) would present the opportunity for much more strategic and cooperative management of land degradation datasets in Small Tropical Caribbean Islands. It is therefore expected to be a vital tool in the treatment of land degradation, and also to assist in creating a network of critical resources to drive further research in the area. This paper reviews the challenges faced by Small Tropical Caribbean Islands when managing land degradation, with special emphasis on Trinidad, and discusses how SDI can be used to better facilitate land degradation management in these areas.
Advancing the Use of Earth Observation Systems for the Assessment of Sustaina...rsmahabir
Abstract: Decisions made on the use of land in Trinidad and Tobago, with little considerations to environmental impact or physical constraints, have resulted in physical, socio-economic, and environmental problems. As a result of the country’s economic progress, urbanisation and development are fragmenting natural areas and reducing the viability of the environment to support the population. Spatial information is a crucial component in the characterisation and examination of the spatio-temporal dynamics and the consequences of the interaction between human and the environment. This information is of critical importance in the development of models to predict future trends in land cover change and therein, best land use practices to be implemented. However, the lack of data at appropriate scales has made it difficult to accurately examine the land use/cover patterns in the country. This paper argues that the gap in data and information can be managed through the adoption of earth observation technology. Moreover, it reports on the developed methodology, and highlights key results of examining the use of geo-spatial images in addressing sustainability issues associated with development. The developed methodology involves several critical steps in using multi-spectral imagery including cloud and cloud shadow removal, image classification and image fusion. Additionally, a method for improving classification performance using high resolution imagery is discussed. The results demonstrated the accuracy, flexibility and cost-effectiveness of these technologies for mapping the land cover and producing other environmental measures and indicators. Further, these results confirmed the effectiveness of this technology in establishing the necessary baseline and support information for sustainable development in the Caribbean region.
Dengue Fever Epidemiology and Control in the Caribbean: A Status Report (2012)rsmahabir
The epidemiology of Dengue fever in the English speaking Caribbean over the last two decades is reviewed. Dengue cases reported to the World Health Organization, Pan American Health Organization, Caribbean Epidemiology Centre and in recent published papers were collated and analysed to determine the incidence and geographical distribution among the various countries. Dengue fever was observed among most Caribbean countries with various intensities of transmission. During 2010 all four dengue serotypes were found co-circulating within the Caribbean islands with crude fatality rates of 6 in Barbados, 4 in Jamaica, 3 in the Bahamas and 2 in Dominica. Similar numbers of males and females from the 20-39 age group were found with DHF but the 10-19 age group shows a slight increase in disease levels. Overall more males were reported with DF/DHF than females. The results show significant (P<0.002) increases in the number of DF/DHF cases and in Ae. aegypti indices during the rainy season compared to the dry season. Little data is available on the density of the Aedes aegypti population in the Caribbean region, and most information comes from Jamaica and Trinidad and Tobago.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Healthy Food Accessibility and Obesity: Case Study of Pennsylvania, USArsmahabir
Abstract-Obesity is a continuing challenge for any town, city or country faced with this problem. Being obese increases your risk of physical disorders such as high blood pressure (BP), high blood cholesterol, diabetes, coronary heart disease, stroke, cancer and poor reproductive health. Higher obesity rates also leads to increased economic burden on society. In order to better understand and control obesity rates the in uence of various factors on its prevalence should be investigated. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships using a combination of socio-economic and physical factor for counties in Pennsylvania (PA), USA for 2010. Our ndings suggest that the rate of obesity is impacted by local spatial variation and its prevalence positively correlated with diabetes, physical inactivity and the distance that a person must travel to get to a healthy food store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was found to signi cantly improve model tting over OLS (AICc = 299.87; r-squared = 0.34). These results indicate that additional factors, including social, cultural and behavioral, are needed to better explain the distribution of obesity rates across PA.
Exploratory space-time analysis of dengue incidence in trinidad: a retrospect...rsmahabir
The increased geographic spread and intensity of dengue is due to numerous factors including, increased urbanization, human migrations and air travel, flooding and global warming. In the Caribbean, outbreaks continue to occur with hyperendemic occurrence of the disease. This is mainly due to the use of reactive programs and limited resources available to control the disease. Using the island of Trinidad as a case study, we show that higher rates of infection occur in areas with a history of dengue incidence. Also, a general pattern in the movement of dengue cases is found leading up to and transitioning away from an epidemic occurrence, and associated with the locations of transportation hubs. These findings can be used to contain the disease in a more efficient and effective manner. Also, few studies have examined the space and time relationship of dengue incidents at local scales in the Caribbean islands. Other islands can adopt the approach used to better allocate resources and understand the disease. This information can then be used to gain regional perspective and understanding about the spatio-temporal persistence of dengue in the Caribbean.
VDIS: A System for Morphological Detection and Identification of Vehicles in ...rsmahabir
With the growth of urban centers worldwide, the number of vehicles in and around these areas has also increased. Traffic-related data plays an important role in spatial planning, for example, optimizing road networks and in the estimation or simulation of air and noise pollution. This information is important as it reflects the changes taking place around us. Additionally, data collected can be used for a wide array of applications including law enforcement, fleet management, and supporting other analyses at varying scales. In this paper, we present a method for the detection and identification of vehicles from low altitude, high spatial resolution Red Blue Green (RGB) images, utilizing both object spectra and image morphology. Results show an identification performance upwards of 62% with false positives occurring from the use of images with sun glare and vehicles with similar spectra values.
Climate Change and Forest Management: Adaptation of Geospatial Technologiesrsmahabir
eraction with the environment, has led to increased concerns about the impact of such disruption on major areas of sustainable development. This has resulted in various innovations in technology, policy and forged alliances at regional and international scales in an effort to reduce humans’ impact on climate. Forests provide a suitable option for reducing the net amount of carbon dioxide in the atmosphere by acting as carbon sinks, thereby forming one part of a more complete solution for combating climate change. At the same time, forests are also sensitive to changes in climate, making sustainable forest management a critical component of present and future climate change strategies. This paper examines the contribution of geospatial technologies in supporting sustainable forest management, emphasizing its use in the classification of forests, estimation of their structure, detecting change and modeling of carbon stocks.
Black holes no more the emergence of volunteer geographic informationrsmahabir
More than one billion people currently live in slums, which are growing at unprecedented rates leading to the rise of vulnerable communities. Slums are usually viewed as areas of extreme poverty and neglect and further, their development as an impediment to progress. Although slums exist in all areas of the world, their presence is most noticeable in the less developed countries of the global south. These countries are among the poorest worldwide as suggested by the Human Development Index and the substantial disbursement of and dependence on international aid. With the added burden of having to absorb the majority of projected population growth, further challenges can be expected at these locations if the situation of slum dwellers does not improve.
Coral Reefs: Challenges, Opportunities and Evolutionary Strategies for Surviv...rsmahabir
Coral reefs are one of the most diverse marine ecosystems on Earth. They are renowned hotspots of species biodiversity and provide home to a large array of marine plants and animals. Over the past 100 years, many tropical regions’ sea surface temperatures have increased by almost 1 °C and are currently increasing at about 1–2 °C per century. Corals have very specific thermal thresholds beyond which their temperature sensitive symbiont Zooxanthellae becomes affected and causes corals to bleach. Mass bleaching has already caused significant losses to live coral in many parts of the world. In the Caribbean, the problem of coral bleaching has especially been problematic, with as much as 90% bleaching in some parts of the Caribbean due to thermal anomalies in some instances. This paper looks at the key role that temperature plays in the health and spatial distribution of coral in the Caribbean. The relationship between coral and symbiont is examined along with some evolutionary strategies necessary to ensure the future survival of coral with the changing climate.
Authoritative and Volunteered Geographical Information in a Developing Countr...rsmahabir
Abstract: With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google’s Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.
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Radar and optical remote sensing data evaluation and fusion; a case study for Washington, DC, USA
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Radar and optical remote sensing data
evaluation and fusion; a case study for
Washington, DC, USA
Terry Idol
a
, Barry Haack
a
& Ron Mahabir
a
a
Department of Geography and Geoinformation Science, George
Mason University, Fairfax, VA, USA
Published online: 20 Mar 2015.
To cite this article: Terry Idol, Barry Haack & Ron Mahabir (2015): Radar and optical remote sensing
data evaluation and fusion; a case study for Washington, DC, USA, International Journal of Image
and Data Fusion
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4. expected that remote sensing will continue to be relied on as a sustainable source of
information on land cover/use.
There has been a tremendous increase in the number of spaceborne remote sensors
over the last years. These systems have provided data with a broad range of spatial,
spectral, temporal and radiometric resolutions. With these expansions in data types, there
has been a much wider set of applications for these data and improvements in derived land
cover/use maps and statistical information. For many years, this technology has been
based on optical, typically multispectral, systems such as Landsat and the French Satellite
Pour I’Observation de la Terre (SPOT). More recently, active microwave or radar has
become more available. These radar systems have some significant advantages over
optical systems in their ability to penetrate cloud cover and have night-time capabilities
(Al-Tahir et al. 2014). This provides the opportunity to collect data in areas, such as low-
latitude tropical regions and high latitudes, where it is difficult to obtain data via other
sensors (Henderson et al. 2002, Li et al. 2012). In the Amazon forest of South America,
for example, the likelihood of capturing optical image scenes with cloud cover rates of
30% or less can be as low as 0% per year (Asner 2001). Mahabir and Al-Tahir (2008) also
report similar issues for the Caribbean region using the island of Trinidad as a case study.
In such locations, radar imagery has tremendous potential for both updating and monitor-
ing land cover/use changes.
Spaceborne radar has recently improved greatly from the single wavelength and single
polarisation, in essence one band, which earlier systems provided. Those systems were
very limited in the amount of surface information that could be extracted (Töyrä
et al. 2001, Dell’Acqua et al. 2003).Newer systems, such as the Japanese Phased Array
type L-band Synthetic Aperture Radar (PALSAR), the Canadian RADARSAT-2 and the
European TerraSar-X and Sentinel sensors, collect information from multiple polarisa-
tions, allowing for much more complex processing and analysis and potentially more
useful spatial information (Sawaya et al. 2010, Sheoran and Haack 2013). In addition,
individual radar sensors may function in different microwave portions of the spectrum,
providing opportunities for comparison and integration.
Polarisation, the orientation of the beam relative to the earth’s surface either vertically
or horizontally, is important to remote sensing scientists as each type of polarisation
provides a different type of information. Polarisation can be altered for both the transmit-
ting and receiving aspects of the process, thus allowing four possible combinations of sent
and received signals; HH – horizontal sent/horizontal received, VV – vertical sent/vertical
received, HV – horizontal sent/vertical received and VH – vertical sent/horizontal
received (Campbell and Wynne 2012). With a quad polarisation sensor, all four combina-
tions are acquired.
One of the important derived values from radar is surface texture, the amount of
smoothness or roughness of a feature. For some features, texture by itself can be useful,
but often it is combined with the original radar data. There are many texture derivatives at
multiple window sizes that can be extracted from an image, potentially creating many
additional bands for analysis (Anderson 1998, Dekker 2003, Herold et al. 2003, 2004,
Lloyd et al. 2004, Amarsaikhan et al. 2007). These additional layers offer different sets of
information that can be used to improve discrimination between land cover/use features in
an image.
Various studies have compared different approaches for combining optical and radar
data for improving discrimination of the earth’s surface features. Pereira et al. (2013)
compared layer stacking and principal component fusion methods for separating different
agricultural land cover/use types in Brazil. Both Palsar and Landsat 5 TM data were used,
2 T. Idol et al.
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5. with better discrimination resulting from layer stacking. Similarly, in Waske and Van Der
Linden (2008), support vector machine and random forest methods were tested with
overall classification accuracy results similar for both methods. Alparone et al. (2004)
developed an intensity-modulated approach, while Eshan (2011) compared Hue Intensity
Saturation and Brovey transformand Le Hégarat-Mascle et al. (1998) applied a Dempster
Shafer approach. These studies and others show improved classification results in the
combined use of optical and radar data compared to the results of individual sensors.
Numerous other methods for multisensor fusion exist, with an excellent review of this
topic found in Luo et al. (2002), Pohl and Van Genderen (1998), Hall and Llinas (1997)
and Ehlers (1991). Many of these approaches applied to the fusion of optical and radar
data look at single areas where heterogeneity is less prevalent, for example, forest or
agricultural areas. This in comparison to the separation of feature types from multiple land
cover/use types. Furthermore, of those studies which have examined multiple land covers/
uses, few have used data gathered from multiple polarisations and from multiple wave-
lengths. Such studies are becoming increasingly important with increased availability of
these data types and with the expansion of human settlements.
The purpose of this research is to compare land cover/use classifications obtained
independently and in combinations of different radar wavelengths, polarisations and
derived texture measures. In addition, an optical image was included in this analysis.
This was an important component of this research since radar data, in comparison to
optical data, are usually captured within a much more limited set of bands (Shiraishi
et al. 2014). It is therefore expected that the combination of radar and radar-derived
texture measures and optical data will lead to improvements in the classification accuracy
of derived land cover/use. Furthermore, the Washington, DC, site used in this study
presents the opportunity to examine a complex landscape which continues to be influ-
enced by many cultures, both within the major city limits and the surrounding landscape.
In Section 2, a brief description of the study site and data used is given. Section 3 provides
the methodology for classifying and determining the accuracy of the results. Section 4
provides results for the various radar, radar-derived texture measures, optical data and
combinations of these for supporting land cover/use mapping, while Section 5 concludes
this paper.
2. Study site and data
The study area is Washington, DC, USA. Aster, Radarsat-2 and Palsar images were
acquired over the study area. The Aster image was collected on 11 March 2009, while
the Radarsat-2 and Palsar quad polarisation data were acquired on 17 July 2009 and
17 April 2007, respectively. These differences in acquisition dates do create some
concerns, but since the primary goal is relative comparison of different data combina-
tions, those concerns should be consistent for all classifications thus allowing valid
conclusions.
Radarsat-2 was launched December 2007 and is the first commercial radar sensor to
acquire C-band quad polarisation imagery. Radarsat-2 offers a wide range of spatial
resolutions that vary based on different beam modes of operation (Canadian Space
Agency 2008). A fine pixel resolution 8 m quad-polarisation image was obtained for
this study. The Palsar satellite was launched in January 2006. Palsar uses L-band radar
with quad polarisation and is supported by Japan Aerospace Exploration Agency (JAXA),
a Japanese Government organisation. The spatial resolution from Palsar was 12.5 m
(JAXA 2006). Aster, an optical instrument on board the Terra satellite, was first launched
International Journal of Image and Data Fusion 3
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6. in December 1999 as a joint venture between the United States and Japan. This sensor
collects earth system data along 14 bands, but only three with the finest spatial resolutions
of 15 m were selected for this analysis; visible and near infrared nadir bands (0.52–
0.86 μm).
The Washington, DC, study area and several surrounding suburban/urban areas (white
and pink tones) are shown in Figure 1. The imagery also includes a significant portion of
forest (green and dark grey tones). Forests are mainly located outside of city limits,
separating most suburban areas and with greater fragmentation of this land cover/use
type in closer proximity to suburban and urban areas. In addition, the Potomac River
(black tones) provides the opportunity to classify water bodies. The Potomac River has a
length of approximately 644 km, with the deepest point at 107 feet. However, a navigable
channel depth of about 24 feet is maintained for most of the downstream portion of the
Washington, DC, area (USGS 1988). The vast majority of high backscatter areas (white
tones) in the radar image were the urban features in and around Washington, DC. As
shown in Figure 1, many urban centres sit alongside the banks of the Potomac in
downtown Washington, DC. Suburban residential areas (pink tones) were present across
much of the scene and demonstrated a mix of high and low radar returns, as would be
expected from a complex landscape of buildings, lawns, trees, roads, etc.
This study site is useful for determining whether different combinations of original
radar and texture measures can provide good urban classifications.
Figure 1. Radarsat-2 composite (HH, VV, HV and HV) image over Washington (approximate size
27 × 31 km).
4 T. Idol et al.
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7. The land cover/use classification types examined for Washington, DC, consisted of
urban, forest, suburban and water, as described by Anderson et al. (1976). The classes
used in this study were generalised and a limited number but for a comparison of methods
and data, they were considered sufficient. At a later research stage, based upon results
from this study, a more detailed definition of classes may be considered.
3. Methodology
Collected images from the various Radarsat-2, Palsar and Aster sensors over the study area
were first reduced to the lowest common boundary between all three products. Images were
then registered to a common geographic coordinate system, Universal Transverse Mercator
Zone 18 N with an earth model of World Geodetic System 1984 and pixels resampled to
10 m using the nearest neighbour algorithm to support uniform analysis of the data. In
addition, the radiometric resolution of all data was consistently set at 8 bits.
Training and truth areas of interest (AOI) polygons were then carefully selected to
prevent as much as possible cross-contamination of class pixels. These polygons were
determined by knowledge of the area, ground reconnaissance, from visual analysis of
the various remote sensing data and use of higher spatial resolution imagery, such as
from Google Earth. Figure 2 shows samples of each land cover/use type collected
from the Aster imagery. These are represented at different scales to assist in visual
Forested Suburban
Urban Water
Figure 2. Optical scenes of Washington, DC, classes from Aster imagery.
International Journal of Image and Data Fusion 5
Downloadedby[GeorgeMasonUniversity]at11:5723March2015
8. differentiation of each type. Training AOIs were used to calibrate, or train the
classification algorithm and were exclusive to this use only, as was the use of the
truth AOIs. The training AOIs identified the spectral characteristics, signatures of each
of the four classes. The truth AOIs, at different locations than the training, were used
to determine the accuracy of the land cover/use classifications. A classification
accuracy of 85% suggested good class and overall thematic accuracy, as recommended
by Congalton and Green (1999) and Anderson et al. (1976). For both calibration and
validation, two to four AOIs were selected for each class with each AOI containing
about 1600 pixels on average. A maximum-likelihood (ML) decision rule was applied
to obtain the classifications. ML is a parametric classifier based on statistical theory. It
is one of the most widely used methods for land cover/use classification (Hansen
et al. 1996, Richards and Jia 2005), making this method an appropriate choice for use
in this research.
Images used throughout the classification process were derived from layer stacking
individual layers to create a single-band image. For example, for the Aster image, all three
visible and near infrared bands were layer stacked. A similar approach was used for
classifying radar (HH, VV, HV and VH bands) and radar-combined products. The next
section presents the results of the various classifications beginning with the independent
Aster and radar images and followed by the various value added, texture evaluations and
data combinations.
4. Results
4.1. Aster classification
Table 1 contains the results for the Aster analysis. The optical data provide an initial
classification against which the radar and radar fusion results can be compared. The
horizontal line near the bottom of each error matrix is the producer’s accuracy for each
class. The column on the right of each matrix presents the user’s accuracy for each class.
The single bolded number on the bottom right of each matrix is the overall thematic
accuracy. The optical land cover/use classification results are good for all classes, ranging
from 87% to 92% in producer’s accuracies and 84% to 100% in user’s accuracies. The
overall accuracy is a very good 90% for the three-band imagery and for a complex rural–
urban interface location. It is interesting that there is not more confusion between forest
and suburban or urban and suburban.
4.2. Radar analysis
Radar often has speckles, random pixels of high or low backscatter, which are, in
essence, errors as a function of the sensor operation. There is considerable and not
Table 1. Error matrix for Aster, Washington, DC.
Water Forest Suburban Urban
Water 4331 0 0 0 1.000
Forest 0 4511 363 18 0.922
Suburban 0 314 4030 402 0.849
Urban 638 68 193 4719 0.840
0.872 0.922 0.879 0.918 0.898
6 T. Idol et al.
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9. consistent literature relative to the need to remove, or at least reduce, the amount of
speckle (Lu et al. 1996, Bouchemakh et al. 2008, Maghsoudi et al. 2012). Using the
radar data for Washington, an analysis was made between the spectral signatures and
thematic classifications of original radar and despeckled radar at both 3 × 3 and 5 × 5
windows using the Lee-Sigma algorithm. The larger window size despeckled data had
higher overall thematic accuracies as would be expected, particularly given the use of
polygons for accuracy assessment, because the despeckling is basically a smoothing
filter. However, the differences were relatively small. The Radarsat-2 original accuracy
was 57%, increasing to 59% with the 5 × 5 filter, and Palsar increased from 72% to
77% upon despeckling. Based on these results, for this study, the radar image would
be despeckled for future classifications while derived texture values would be obtained
from the original radar data.
Table 2 contains the spectral signatures of two polarisations for the different land
cover/use classes for the despeckled Palsar image. Only the HH and HV bands are
shown in Table 2 as both HH and VV, and HV and VH results were very similar.
These signatures can provide information on how well the different classes are
statistically separated, giving insight into how well classifications might be. As
would be expected, the larger window sizes have lower standard deviations but are
minimally reduced. The class mean digital number (DN) values, especially for the HH
polarisation, are reasonably different, especially given the standard deviations.
However, other than the low DN values for water, the HV classes overlap in spectral
space. The spectral signatures for the despeckled Radarsat-2 classes (not shown)
produced a pattern similar to the despeckled Palsar, with the notable exception that
the Palsar data had overall lower standard deviation values for each land cover/use
class.
Table 3 contains the error matrix of the classification for the 5 × 5 window despeckled
Radarsat-2 and Palsar images. The Palsar overall accuracy is much higher than the
Table 2. Spectral signatures of Washington, despeckled Palsar imagery.
Palsar imagery 3 × 3 Window 5 × 5 Window
Land cover/use classes HH HV HH HV
Water "X 18.47 3.36 18.47 3.35
σ 2.02 0.53 1.66 0.5
Min. value 12 2 13 2
Max. value 25 4 23 4
Forest "X 25.93 14.22 25.91 14.22
σ 4.75 2.14 4.29 1.87
Min. value 15 8 17 9
Max. value 45 22 42 21
Suburban "X 33.81 14.32 33.85 14.26
σ 6.89 3.14 6.18 2.55
Min. value 16 7 17 8
Max. value 59 37 57 28
Urban "X 43.83 14.98 43.66 14.91
σ 9.94 3.20 8.76 2.75
Min. value 22 7 25 7
Max. value 96 28 86 25
International Journal of Image and Data Fusion 7
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10. Radarsat-2 image. This could be a function of wavelength, spatial resolution, date, the
properties of the despeckled images, as previously mentioned, or a combination of these
factors. Nonetheless, the differences are considerable. Both sensors, as would be expected,
easily delineate water but the misclassification of water with suburban, as indicated by the
producer’s accuracy for Radarsat-2, is surprising. The confusion between forest and
suburban in both data sets is expected, but the Palsar urban delineations were better
than anticipated.
4.3. Texture analysis
Traditional digital image classification methodologies are based only upon the use of the
spectral characteristics of the data, thus ignoring any spatial information in the collected
data (Maillard 2003). Some landscape features, such as residential or urban areas, are
more easily distinguished by their spatial characteristics than spectral (Solberg and
Anil 1997, Nyoungui et al. 2002). Ignoring the full complement of data collected, spectral
and spatial, creates challenges for the accurate classification of some land cover/use
classes. The spatial arrangement of an image, to some degree, can be extracted as textural
information from the pixels and is particularly useful for radar (Kurosu et al. 1999, Chen
et al. 2004, Champion et al. 2008, Cervone and Haack 2012). Radar texture was therefore
an important component of this study, with most measures used today based on the work
of Haralick et al. (1973).
Based upon prior research, the variance measure of texture was selected for this study
(Haack and Bechdol 2000). Variance texture measures were extracted for four different
window sizes for each band of the original, not despeckled, Radarsat-2 and Palsar data.
The window sizes were 5 × 5, 9 × 9, 13 × 13 and 17 × 17. The best window sizes are a
function of the spatial resolution of the sensor and the specific landscape characteristics
(Villiger 2008). Classifications were obtained for each texture window size and their error
matrices contained in Tables 4 and 5. Equation (1) shows the method used for calculating
variance measures used in this study.
Table 3. Error matrices for Washington classification using despeckled 5 × 5 window.
Water Forest Suburban Urban
Radarsat 2
Water 4101 1 2 3 0.999
Forest 0 2365 1661 777 0.492
Suburban 598 2145 2507 1785 0.356
Urban 270 382 416 2574 0.707
0.825 0.483 0.547 0.501 0.590
Palsar
Water 4962 0 0 0 1.000
Forest 0 3605 931 77 0.781
Suburban 0 1228 2250 843 0.521
Urban 7 60 1405 4219 0.741
0.999 0.737 0.491 0.821 0.768
8 T. Idol et al.
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11. Variance ¼
Æ Xij À "X
À Á2
n À 1
(1)
where Xij = DN value of pixel (i, j)
n = number of pixels in window
"X = mean of moving window.
Table 4 shows that texture measures for the Radarsat-2 image produced much higher
accuracies than the original data (59%). However, in the case of the Palsar image, none of
the texture measures were able to generate a land cover/use classification accuracy that
was as high as the classification results for the original image (77%). The observed
differences in classification results were unexpectedly high for smaller window sizes.
These differences are likely a result of how the different radar bands interact with the
landscape features.
There is a pattern, with only one minor exception, in both the Radarsat-2 and Palsar
results. As the window size gets larger, the overall accuracy of the land cover/use
classification improves. In the Radarsat-2 texture measures, the overall accuracy improves
from 65% at a window size of 5 × 5 to 71% with a window of 13 × 13. There is a slight
decline at the largest window size of 17 × 17. For the Palsar image, the results are similar.
The overall accuracy for the texture measures increased from 64% with a window of 5 × 5
to 75% for a window of 17 × 17.
Table 4. Washington error matrices of Radarsat-2 variance texture.
Water Forest Suburban Urban
5 × 5 Window
Water 4808 8 0 18 0.995
Forest 44 4003 2951 958 0.503
Suburban 97 715 1046 1268 0.335
Urban 20 167 589 2895 0.789
0.968 0.818 0.228 0.563 0.651
9 × 9 Window
Water 4590 0 0 0 1.000
Forest 0 4038 2637 252 0.583
Suburban 379 682 1245 1063 0.370
Urban 0 173 704 3824 0.813
0.924 0.825 0.271 0.744 0.699
13 × 13 Window
Water 4215 0 0 0 1.000
Forest 0 3882 2300 94 0.619
Suburban 754 876 1396 673 0.377
Urban 0 135 890 4372 0.810
0.848 0.793 0.304 0.851 0.708
17 × 17 Window
Water 3397 0 0 0 1.000
Forest 0 3685 2112 4 0.635
Suburban 1572 1080 1478 428 0.324
Urban 0 128 996 4707 0.807
0.684 0.753 0.322 0.916 0.677
International Journal of Image and Data Fusion 9
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12. The Radarsat-2 texture measure producer’s accuracy show interesting fluctuations
in the forest, suburban and urban classes when compared to the values obtained with
the despeckled 5 × 5 original image. This is understandable as texture is very
different from backscatter. The forest and urban producer’s accuracy increased but
that of the suburban decreased with texture. The user’s accuracy values were more
consistent.
There was an anomaly in one class’s accuracy in the Radarsat-2 texture results.
The water producer’s accuracy decreased significantly between the window size of
13 × 13 and that of a window size of 17 × 17, from 85% to 68%. The reason for the
decrease in water accuracy can be found by visually analysing the original Radarsat-2
imagery. Intense urban and suburban features that surround portions of the Potomac
River have been ‘ghosted’ or reflected onto the river compounded by the larger
window size for texture, which will therefore include more land-based pixels and a
less unique signature.
The overall classification result of the Palsar despeckled 5 × 5 image was 77%. The
classification result from the texture measure generated from the Palsar original image
with a window size of 17 × 17 is 75%, a decrease of 2%. The classification performed
with the despeckled 5 × 5 image does slightly worse in the producer’s accuracy for the
water, suburban and urban classes. Conversely, the texture measure classification does
slightly better in the producer’s accuracy for the forest class. These differences, however,
are minimal.
Table 5. Washington error matrices of Palsar variance texture.
Water Forest Suburban Urban
5 × 5 Window
Water 4760 108 41 27 0.964
Forest 175 3948 2392 1038 0.523
Suburban 7 613 1142 1427 0.358
Urban 27 224 1011 2647 0.677
0.958 0.807 0.249 0.515 0.638
9 × 9 Window
Water 4784 2 0 0 1.000
Forest 168 3917 1978 480 0.599
Suburban 0 801 1490 1362 0.408
Urban 17 173 1118 3297 0.716
0.963 0.801 0.325 0.642 0.689
13 × 13 Window
Water 4830 0 0 0 1.000
Forest 139 3800 1664 228 0.652
Suburban 0 929 1624 1283 0.423
Urban 0 164 1298 3628 0.713
0.972 0.777 0.354 0.706 0.709
17 × 17 Window
Water 4884 0 0 0 1.000
Forest 85 3711 1489 109 0.688
Suburban 0 936 2159 1016 0.525
Urban 0 246 938 4014 0.772
0.983 0.758 0.471 0.781 0.754
10 T. Idol et al.
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13. 4.4. Combining despeckled radar with texture
The 5 × 5 despeckled original radar images were integrated with the best of the texture
measures for each sensor and then classified, that is, the 13 × 13 and 17 × 17 variance
measures for the despeckled Radarsat-2 and Palsar data, respectively. Table 6 contains the
results of these combinations for both radar sensors.
The Radarsat-2 combination provided an overall accuracy of 71%, an improvement
when compared to the despeckled-only radar image classification of 59%, an increase of
12%. The overall classification accuracy of Palsar original despeckled image combined
with the best texture measures image improved slightly when compared to the classifica-
tion of the single Palsar original despeckled image alone, from 77% to 78%.
The producer’s accuracy of the water class was high, 84% and 100%, in the two radar
wavelengths. By adding texture measures to the original imagery, the forest and urban
classes were able to perform much better in both the producer’s and user’s accuracies,
when compared to the original Radarsat-2 imagery. The texture measures in these classes,
when combined with the original image, greatly enhance the classification results. The
results in the Palsar image were lower, as the overall classification increased only by 1%.
The producer’s accuracy in the urban class did increase by 7%, but that of the forest class
actually decreased by a nominal 1%. Palsar continues to provide better results than
Radarsat-2.
4.5. Combining multiple wavelength radar images
The recent increase in types of spaceborne radar allowed this analysis to include classify-
ing radar images from two different portions of the electromagnetic spectrum. The Palsar
sensor collects data in the L-band, while the Radarsat-2 collects data in the C-band. Both
of the images used in this analysis were despeckled with a 5 × 5 window.
The combined Washington Palsar and Radarsat-2 images had a slight increase in
overall accuracy to 78% (Table 7), when compared to the 77% overall accuracy result that
was achieved when classifying the despeckled Palsar image alone. However, there are
some interesting class differences with less range in producer’s and user’s accuracies. For
the combined radar, the producer’s accuracies varied from 71% to 94% while in the
original Palsar, the range was from 49% to 100%. The suburban class for original Palsar
Table 6. Error matrices of Washington original despeckled imagery combined with the best
derived texture measure.
Water Forest Suburban Urban
Water 4177 0 0 0 1.000
Forest 3 3535 1859 52 0.649
Suburban 787 1203 1867 718 0.408
Urban 2 155 860 4369 0.811
0.841 0.722 0.407 0.850 0.712
Palsar Original and Texture 17 × 17
Water 4959 0 0 0 1.000
Forest 0 3551 1048 4 0.771
Suburban 0 1226 2209 539 0.556
Urban 10 116 1329 4596 0.760
0.998 0.726 0.482 0.894 0.782
International Journal of Image and Data Fusion 11
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14. increased from 49% to 71% in the fused radar. These reduced class-by-class variations
support the integration of multi-wavelength radar for land cover/use mapping.
4.6. Combining optical and radar images
The data acquired for this study provide an opportunity to integrate the radar and texture
measures with the Aster multispectral image. The fusion of different radar wavelengths
with optical imagery in land cover/use classification is a relatively new area of research
(Santos and Messina 2008, Amarsaikhan et al. 2012). This may be in part due to the much
lower accessibility of radar data compared to optical data available to scientists and
researchers alike.
The Washington Aster image only land cover/use classification overall accuracy was
90%. Whereas the best classification accuracy for a radar data set was achieved through
the use of the Palsar, 77%. The addition of the Palsar imagery to the Aster increased the
overall accuracy to 93% (Table 8). The Radarsat-2 texture measure with a window size of
13 × 13 produced the best overall accuracy result for the Washington imagery texture
Table 7. Washington multi-wavelength error matrices combining Radarsat 2 and Palsar.
Water Forest Suburban Urban
Water 4650 0 0 0 1.000
Forest 0 3269 699 75 0.809
Suburban 0 1596 3252 1007 0.555
Urban 319 28 635 4057 0.805
0.936 0.668 0.709 0.789 0.777
Table 8. Error matrices of Washington multispectral optical, radar and texture combinations.
Water Forest Suburban Urban
Water 4913 0 0 0 1.000
Forest 0 4442 184 4 0.959
Suburban 0 432 4142 427 0.828
Urban 56 19 260 4708 0.934
0.989 0.908 0.903 0.916 0.929
Aster and Radarsat-2 Texture 13 × 13
Water 4805 0 0 0 1.000
Forest 0 3887 209 0 0.949
Suburban 16 931 4162 465 0.747
Urban 148 75 215 4674 0.914
0.967 0.794 0.908 0.910 0.895
Aster and Palsar and Radarsat-2 Texture 17 × 17
Water 4966 0 0 0 1.000
Forest 0 3793 155 0 0.961
Suburban 0 1034 3912 359 0.737
Urban 3 66 519 4780 0.890
0.999 0.775 0.853 0.930 0.891
12 T. Idol et al.
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15. measures (71%). This layer was then combined with the Aster data, which yielded an
overall accuracy of 90%.
Finally, the best texture measure, which again, was the Radarsat-2 with a window size
of 13 × 13 and the best of the original radar, which was the Palsar image, were layer
stacked with the Aster data. These combined data layers were analysed and the land
cover/use classification was generated to produce an overall accuracy of 89%.
For the Washington, DC, location and its land cover/use classes, the combination of
the radar or derived radar texture measures did not improve overall accuracies much over
the original Aster. Given that the Aster independently had a classification of 90%, there
was little opportunity for improvement. There are, however, some specific class improve-
ments with the sensor fusion, such as the producer’s accuracy for water increasing from
87% to 100% in Aster with sensor integration.
5. Summary
Land cover/use information represents an important resource for tracking humans’ impact
on the earth’s surface. Without adequate land cover/use information, decision-makers
often fail to make reliable decisions concerning the sustainable planning and management
of land resources. This in turn can have disabling effects, both medium and long term, on
countries’ self-sustainability.
The most common method of collecting land cover/use data is the use of optical
sensors on board aerial and spaceborne platforms. These methods, although largely
successful, continue to be impacted by cloud cover, especially low tropical and high-
latitude locations, presenting a challenge for continuous observation and monitoring of
land resources. Radar, still a relatively new area of research to land cover/use mapping
(Hoekman et al. 2010), has the potential to overcome these challenges. The electromag-
netic waves of radar are almost not influenced by atmospheric interference and provide
all-weather land observation data. As these data become increasingly available, it is
expected that there will be an increased need for studies examining the suitability of
radar, both as a surrogate and as a complementary source of optical data, for land cover/
use mapping in different parts of the world.
In this study, the potential of using radar for supporting land cover/use mapping was
examined. Of the two radar sensors evaluated, the original Palsar data produced much
better classification results when compared to Radarsat-2. Texture, a common tool used
widely in land cover research, was also evaluated. Results showed that derived radar
texture values were variable in their ability to improve classifications. The Radarsat-2
texture measures resulted in better classifications than the despeckled original image by
12%. Further analysis showed that overall, the Palsar C-band did not perform as well as
the Radarsat-2L-band when generating classifications while using a texture measure.
These results are consistent with the findings of Li et al. (2012), comparing L-band and
C-band radar over Brazil, a humid tropical area, relative to the Washington, DC, location
examined in this research. Also interesting and consistent with the literature, the best
classification accuracy improvements were seen in the urban class using Radarsat-2
imagery texture. Urban spaces, known for their difficulty in mapping because of their
complex mix of human-transformed properties, can therefore benefit from the use of radar
to support land cover/use mapping of this class. This is especially important given the
accelerated growth of many such areas over the last 50 years.
The combination of radar and radar-derived texture measures was also explored. The
classification results of the combined original radar and texture images showed varied
International Journal of Image and Data Fusion 13
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16. increases when compared to the overall accuracy of the despeckled-only radar image
classifications. There was virtually no improvement for Palsar but a 7% increase from the
best Radarsat-2 texture when the original was combined with this measure. When the
radar images from two different portions of the electromagnetic spectrum were combined,
this resulted in no improvement over the use of independent Palsar. However, the initial
Palsar results were quite good.
Finally, this study in part reinforced the value of optical imagery. The results for the
classifications using the independent Aster imagery were excellent. Even when optical
imagery is available, radar imagery can help improve the classification results. When the
radar imagery was added to the Aster optical image, the overall accuracy improved, but
marginally, from 90% to 93%. However, the water producer class accuracies were higher
than the optical alone and the urban equal to the optical. For the Washington, DC, data,
independent radar sensor land cover/use classification accuracies do not compete with that
of optical imagery. However, the overall accuracy of radar results of 78% would be very
useful in those regions of the world where cloud cover or other factors limit the avail-
ability of optical acquisition.
Several limitations were also identified during the course of this research, which form
part of improvements for future research. First, only few generic land cover/ use classes
were examined. Although results were generally good for the combination of optical and
radar data, both overall and for individual classes, the classes selected may not be
appropriate for other areas of study which may have different definitions for these classes.
Also, these classes may not be appropriate for better understanding the overall influencers
of land cover/use change taking place on the ground. Investigation of more detailed
classes is therefore needed, which may lead to results different from those obtained in
this study. Second, only one classification method was investigated, the ML decision rule.
Other methods of classification, such as support vector machines and random forest,
should also be investigated and results compared for determining the most suitable
method. Third, this study utilised only one measure for texture. Additional measures
should be examined and compared to produce more conclusive results as to the most
suitable texture measure for use. Finally, several studies have already investigated the use
of multidate radar as a possible source for improving classification results (Le Hegarat-
Mascle et al. 2000, Shao et al. 2001, Chust et al. 2004). Further examination of these
types of data, both as a single data source and as a complementary source to optical data,
should also be investigated.
Acknowledgements
The authors would like to thank the following organisations for providing the imagery used in this
research. Radarsat-2 images were provided by the Canadian Space Agency under project 3126 of the
Science and Operational Application Research for RADARSAT-2 programme. The Alaska Space
Facility, under sponsorship from the NASA, provided the PALSAR imagery. Finally, the NASA
Land Processes Distributed Active Archive Center at the USGS/Earth Resources Observation and
Science (EROS) Center provided the ASTER imagery.
Disclosure statement
No potential conflict of interest was reported by the authors.
14 T. Idol et al.
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17. Funding
Additional support was provided through grants received by the Department of Geography and
Geoinformation Science at George Mason University.
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