International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Classification of Multi-date Image using NDVI valuesijsrd.com
Advance Wide Field Sensor (AWiFS) of IRS P6 is an improved version of WiFS of IRS-1C/1D. AWiFS operates in four spectral bands identical to LISS III (Low-Imaging Sensing Satellite). Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements. These indexes can be used to prediction of classes of Remote Sensing (RS) images. In this paper, we will classify the AWiFS image on NDVI values of 5 different date's images (Captured by AWiFS satellite). For classifying images, we will use an algorithm called Sum of Squared Difference (SSD). It will compare the clustered image with the Reference image based on SSD and the best match on the basis of SSD algorithm, it will classify the image. It is simple 1 step process, which will be faster compared to the classical approach.
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.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Datacsandit
Hyperspectral images provide detailed spectral info
rmation with more than several hundred
channels. On the other hand, the high dimensionalit
y in hyperspectral images also causes to
classification problems due to the huge ratio betwe
en the number of training samples and the
features. In this paper, Lyapunov Exponents (LEs) a
re used to determine chaotic-type structure
of EO- 1 Hyperion hyperspectral image, a mixed fore
st site in Turkey. Experimental results
demonstrate that EO-1 Hyperion image has a chaotic
structure by checking distribution of
Lyapunov Exponents (LEs) and they can be used as d
iscriminative features to improve
classification accuracy for hyperspectral images.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Classification of Multi-date Image using NDVI valuesijsrd.com
Advance Wide Field Sensor (AWiFS) of IRS P6 is an improved version of WiFS of IRS-1C/1D. AWiFS operates in four spectral bands identical to LISS III (Low-Imaging Sensing Satellite). Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements. These indexes can be used to prediction of classes of Remote Sensing (RS) images. In this paper, we will classify the AWiFS image on NDVI values of 5 different date's images (Captured by AWiFS satellite). For classifying images, we will use an algorithm called Sum of Squared Difference (SSD). It will compare the clustered image with the Reference image based on SSD and the best match on the basis of SSD algorithm, it will classify the image. It is simple 1 step process, which will be faster compared to the classical approach.
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.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Datacsandit
Hyperspectral images provide detailed spectral info
rmation with more than several hundred
channels. On the other hand, the high dimensionalit
y in hyperspectral images also causes to
classification problems due to the huge ratio betwe
en the number of training samples and the
features. In this paper, Lyapunov Exponents (LEs) a
re used to determine chaotic-type structure
of EO- 1 Hyperion hyperspectral image, a mixed fore
st site in Turkey. Experimental results
demonstrate that EO-1 Hyperion image has a chaotic
structure by checking distribution of
Lyapunov Exponents (LEs) and they can be used as d
iscriminative features to improve
classification accuracy for hyperspectral images.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
We illustrate unsupervised and supervised learning algorithms that accurately classify the lithological variations in the 3D seismic data. We demonstrate blind source separation techniques such as the principal components (PCA) and noise adjusted principal
components in conjunction with Kohonen Self organizing maps to produce superior unsupervised classification maps.
Further, we utilize the PCA space training in Maximum likelihood (ML) supervised classification. Results demonstrate that the ML supervised classification produces an improved classification of the facies in the 3D seismic dataset from the Anadarko basin in central Oklahoma.
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...drboon
To address the gap in bridging global and smaller modelling scales, downscaling approaches have been reported as an appropriate solution. Downscaling on its own is not wholly adequate in the quest to produce local phenomena, and in this paper we use a physical downscaling method combined with data assimilation strategies, to obtain physically consistent land surface condition prediction. Using data assimilation strategies, it has been demonstrated that by minimizing a cost function, a solution utilizing imperfect models and observation data including observation errors is feasible. We demonstrate that by assimilating lower frequency passive microwave brightness temperature data using a validated theoretical radiative transfer model, we can obtain very good predictions that agree well with observed conditions.
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...ADEIJ Journal
Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is predicted first based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate manner with current training samples. This approach can be further applied as sequential image data, with only a small number of training samples, which are being required from each image. This method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction technique the Signatures are being generated for the input images. The generated Signatures are used for the Training purposes. SVM Classification is used for classifying the images. The final results describes that the leverage of a priori information from previous images will provide advantageous improvement for future images in multi temporal image classification.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Supervised machine learning based dynamic estimation of bulk soil moisture us...eSAT Journals
Abstract In this paper artificial neural network based sensor informatics architecture has been investigated; including proposed continuous daily estimation of area wise surface soil moisture using cosmic ray sensor’s neutron count time series. Study was conducted based on cosmic ray data available from two Australian locations. The main focus of this study was to develop a data driven approach to convert neutron counts into area wise ground surface soil moisture estimates. Independent surface soil moisture data from the Australian Water Availability Project (AWAP) was used as ground truth. A comparative study using five different types of neural networks, namely, Feed Forward Back Propagation (FFBPN), Multi-Layer Perceptron (MLPN), Radial Basis Function (RBFN), Elman (EN), and Probabilistic networks (PNN) was conducted to evaluate the overall soil moisture estimation accuracy. Best performance from the Elman network outperformed all other neural networks with 94% accuracy with 92% sensitivity and 97% specificity based on Tullochgorum data. Overall high accuracy proved the effectiveness of the Elman neural network to estimate surface soil moisture continuously using cosmic ray sensors. Index Terms: Artificial Neural Network, Surface Soil Moisture, Cosmic Ray Sensors, Neutron Counts.
P.S.Jagadeesh Kumar, Tracy Lin Huan, Xianpei Li, Yanmin Yuan. (2018) ‘Panchromatic and Multispectral Remote Sensing Image Fusion using Particle Swarm Optimization of Convolutional Neural Network for Effective Comparison of Bucolic and Farming Region’, Earth Science and Remote Sensing Applications, Series of Remote Sensing/Photogrammetry, Vol. 43, pp.1-31, Springer.
Soil Classification Using Image Processing and Modified SVM Classifierijtsrd
Recently the use of soil classification has gained more and more importance and recent direction in research works indicates that image classification of images for soil information is the preferred choice. Various methods for image classification have been developed based on different theories or models. In this study, three of these methods Maximum Likelihood classification MLC , Sub pixel classification SP and Support Vector machine SVM are used to classify a soil image into seven soil classes and the results compared. MLC and SVM are hard classification methods but SP is a soft classification. Hardening of soft classifications for accuracy determination leads to loss of information and the accuracy may not necessary represent the strength of class membership. Therefore, in the comparison of the methods, the top 20 compositions per soil class of the SP were used instead. Results from the classification, indicated that output from SP was generally poor although it performs well with soils such as forest that are homogeneous in character. Of the two hard classifiers, SVM gave a better output than MLC. Priyanka Dewangan | Vaibhav Dedhe "Soil Classification Using Image Processing and Modified SVM Classifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18489.pdf
REGION CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGESADEIJ Journal
The change detection in remote sensing images remains an important and open problem for damage assessment. A new change detection method for LANSAT-8 images based on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel Transformation transfers one image from its original feature space (e.g., gray space) to another feature space (e.g., spectral space) in pixel-level to make the pre-event images and post-event images to be represented in a common space or projection space for the convenience of change detection. HPT consists of two operations, i.e., forward transformation and backward transformation. In the forward transformation, each pixel of pre-event image in the first feature space is taken and will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with the noise tolerance is produced to determine the mapping pixel using K-nearest neighbours technique. Once the mapping pixels of pre-event image are identified, the difference values between the mapping image and the post-event image can be directly generated. Then the similar work is done for backward transformation to combine the post-event image with the first space, and one more difference value for each pixel will be generated. Then, the two difference values are taken and combined to improve the robustness of detection with respect to the noise and heterogeneousness of images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed to divide the integrated difference values into two clusters- changed pixels and unchanged pixels. This detection results may contain few noisy regions as small error detections, and a spatial-neighbor based noise filter is developed to reduce the false alarms and missing detections. The experiments for change detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given to validate the percentage of the changed regions in the proposed method.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
We illustrate unsupervised and supervised learning algorithms that accurately classify the lithological variations in the 3D seismic data. We demonstrate blind source separation techniques such as the principal components (PCA) and noise adjusted principal
components in conjunction with Kohonen Self organizing maps to produce superior unsupervised classification maps.
Further, we utilize the PCA space training in Maximum likelihood (ML) supervised classification. Results demonstrate that the ML supervised classification produces an improved classification of the facies in the 3D seismic dataset from the Anadarko basin in central Oklahoma.
A Land Data Assimilation System Utilizing Low Frequency Passive Microwave Rem...drboon
To address the gap in bridging global and smaller modelling scales, downscaling approaches have been reported as an appropriate solution. Downscaling on its own is not wholly adequate in the quest to produce local phenomena, and in this paper we use a physical downscaling method combined with data assimilation strategies, to obtain physically consistent land surface condition prediction. Using data assimilation strategies, it has been demonstrated that by minimizing a cost function, a solution utilizing imperfect models and observation data including observation errors is feasible. We demonstrate that by assimilating lower frequency passive microwave brightness temperature data using a validated theoretical radiative transfer model, we can obtain very good predictions that agree well with observed conditions.
CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR M...ADEIJ Journal
Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is predicted first based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate manner with current training samples. This approach can be further applied as sequential image data, with only a small number of training samples, which are being required from each image. This method uses LANSAT 8 images for Training and Testing processes. First, using the Classifier Prediction technique the Signatures are being generated for the input images. The generated Signatures are used for the Training purposes. SVM Classification is used for classifying the images. The final results describes that the leverage of a priori information from previous images will provide advantageous improvement for future images in multi temporal image classification.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Supervised machine learning based dynamic estimation of bulk soil moisture us...eSAT Journals
Abstract In this paper artificial neural network based sensor informatics architecture has been investigated; including proposed continuous daily estimation of area wise surface soil moisture using cosmic ray sensor’s neutron count time series. Study was conducted based on cosmic ray data available from two Australian locations. The main focus of this study was to develop a data driven approach to convert neutron counts into area wise ground surface soil moisture estimates. Independent surface soil moisture data from the Australian Water Availability Project (AWAP) was used as ground truth. A comparative study using five different types of neural networks, namely, Feed Forward Back Propagation (FFBPN), Multi-Layer Perceptron (MLPN), Radial Basis Function (RBFN), Elman (EN), and Probabilistic networks (PNN) was conducted to evaluate the overall soil moisture estimation accuracy. Best performance from the Elman network outperformed all other neural networks with 94% accuracy with 92% sensitivity and 97% specificity based on Tullochgorum data. Overall high accuracy proved the effectiveness of the Elman neural network to estimate surface soil moisture continuously using cosmic ray sensors. Index Terms: Artificial Neural Network, Surface Soil Moisture, Cosmic Ray Sensors, Neutron Counts.
P.S.Jagadeesh Kumar, Tracy Lin Huan, Xianpei Li, Yanmin Yuan. (2018) ‘Panchromatic and Multispectral Remote Sensing Image Fusion using Particle Swarm Optimization of Convolutional Neural Network for Effective Comparison of Bucolic and Farming Region’, Earth Science and Remote Sensing Applications, Series of Remote Sensing/Photogrammetry, Vol. 43, pp.1-31, Springer.
Soil Classification Using Image Processing and Modified SVM Classifierijtsrd
Recently the use of soil classification has gained more and more importance and recent direction in research works indicates that image classification of images for soil information is the preferred choice. Various methods for image classification have been developed based on different theories or models. In this study, three of these methods Maximum Likelihood classification MLC , Sub pixel classification SP and Support Vector machine SVM are used to classify a soil image into seven soil classes and the results compared. MLC and SVM are hard classification methods but SP is a soft classification. Hardening of soft classifications for accuracy determination leads to loss of information and the accuracy may not necessary represent the strength of class membership. Therefore, in the comparison of the methods, the top 20 compositions per soil class of the SP were used instead. Results from the classification, indicated that output from SP was generally poor although it performs well with soils such as forest that are homogeneous in character. Of the two hard classifiers, SVM gave a better output than MLC. Priyanka Dewangan | Vaibhav Dedhe "Soil Classification Using Image Processing and Modified SVM Classifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18489.pdf
REGION CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGESADEIJ Journal
The change detection in remote sensing images remains an important and open problem for damage assessment. A new change detection method for LANSAT-8 images based on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel Transformation transfers one image from its original feature space (e.g., gray space) to another feature space (e.g., spectral space) in pixel-level to make the pre-event images and post-event images to be represented in a common space or projection space for the convenience of change detection. HPT consists of two operations, i.e., forward transformation and backward transformation. In the forward transformation, each pixel of pre-event image in the first feature space is taken and will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with the noise tolerance is produced to determine the mapping pixel using K-nearest neighbours technique. Once the mapping pixels of pre-event image are identified, the difference values between the mapping image and the post-event image can be directly generated. Then the similar work is done for backward transformation to combine the post-event image with the first space, and one more difference value for each pixel will be generated. Then, the two difference values are taken and combined to improve the robustness of detection with respect to the noise and heterogeneousness of images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed to divide the integrated difference values into two clusters- changed pixels and unchanged pixels. This detection results may contain few noisy regions as small error detections, and a spatial-neighbor based noise filter is developed to reduce the false alarms and missing detections. The experiments for change detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given to validate the percentage of the changed regions in the proposed method.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
he data obtained from remote sensing satellites fu
rnish information about the land at varying resolut
ions
and has been widely used for change detection studi
es. There exist a huge number of change detection
methodologies and techniques with the continual eme
rgence of new ones. This paper provides a review of
pixel based and object-based change detection techn
iques in conjunction with the comparison of their
merits and limitations. The advent of very-high-res
olution remotely sensed images, exponentially incre
ased
image data volume and multiple sensors demand the p
otential use of data mining techniques in tandem
with object-based methods for change detection
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
Detection of urban tree canopy from very high resolution imagery using an ob...IJECEIAES
Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
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A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
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Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
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Af33174179
1. Ms. Neha Bhatt, Mr. IndrJeet Rajput, Mr. Vinitkumar Gupta / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.174-179
174 | P a g e
Classification of Multi-date, Tempo-Spectral data using NDVI
values
1
Ms. Neha Bhatt, 2
Mr. IndrJeet Rajput, 3
Mr. Vinitkumar Gupta
1
Department of Computer EngineeringGTU UniversityIndia
2,3
Department of Computer EngineeringHGCEIndia
Abstract— NASA launched the Earth
Observing System's flagship satellite "Terra,"
named for Earth, on December 18, 1999. Terra
has been collecting data about Earth's changing
climate Normalized Difference Vegetation
Index (NDVI) is a simple graphical indicator that
can be used to analyze remote sensing
measurements. These indexes can be used to
prediction of classes of Remote Sensing (RS)
images. In this paper, we will classify the Terra
image on NDVI values of 5 different date’s
images (Captured by Terra satellite). For
classifying images, we will use formulae, which is
based on similarity measure. It will compare the
clustered image with the Reference image based
on the equation, it will classify the image. It is
simple process, which can classify much faster.
Keywords-Terra, Normalized Difference Vegetation
Index (NDVI), Remote Sensing, Multi date.
I. INTRODUCTION
The evaluation of the tempo -spectral data is very
useful in environment field [1]. In this paper, we
propose the technique for finding similarity based on
reference images. The Vegetation Index (VI) based
on the image taken through Remote Sensing can be
used to identify the objects [1]. Normalized
Difference Vegetation Index (NDVI) is one of the
popular Vegetation Index. In this paper, we are using
Terradata to compute the similarity.
Terra carries five state-of-the-art sensors that
have been studying the interactions among the Earth's
atmosphere, lands, oceans, and radiant energy. Each
sensor has unique design features that will enable
scientists to meet a wide range of science objectives.
The five Terra onboard sensors are:
ASTER, or Advanced Spaceborne Thermal
Emission and Reflection Radiometer
CERES, or Clouds and Earth's Radiant
Energy System
MISR, or Multi-angle Imaging
Spectroradiometer
MODIS, or Moderate-resolution Imaging
Spectroradiometer
MOPITT, or Measurements of Pollution in
the Troposphere
Because Terra's five sensors share a platform,
they collect complimentary observations of Earth's
surface and atmosphere. These varying perspectives
of the same event can yield unique insights into the
processes that connect Earth's systems.
Figure1: Terra Sensor
NDVI value of an image can be calculated based
upon the following formulae.
REDIR
REDIR
NDVI
Where IR (Infra-Red) denotes the value in the
Infra-Red band and RED denotes the value in Red
band. As image consist of many bands, NDVI uses
these 2 bands for its calculation.
Template matching is one of the simple
techniques used from past many decades. It is a basic
technique for image as it can answer too many
questions related to image [2]. We give the faster
algorithm Sum of Squared Difference on NDVI
values of Remote Sensing data. Also the intention of
this paper is not to say that this measure opposes the
other measures.
II. RELATED WORK
Integration of different and complementary
sensor images such as optical and radar remotely-
sensed images has been widely used for cloud cover
removal [1] and achieving better scene interpretation
accuracy [2, 3]. The integration may be done by
image mosaicking or data fusion which are
accomplished either at the preprocessing stage [4] or
the postprocessing stage [2]. Sensor-specific
classifiers are commonly used. For example,
classifiers based on image tonal or spectral features
are used to classify optical images [2]. In other
cases, classifiers based on texture features are used
for recognizing cloud types [5] and improving the
urban area classification result for optical images.
For radar image interpretation, classifiers based on
various texture models were used [6, 7, 8], but
problems may arise if homogeneous-region land
cover objects exist in the radar image [9].
2. Ms. Neha Bhatt, Mr. IndrJeet Rajput, Mr. Vinitkumar Gupta / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.174-179
175 | P a g e
We have observed that both optical and
radar images consist of homogeneous and textured
regions. Aregion is considered as homogeneous if
the local variance of gray level distribution is
relatively low, and a region is considered as textured
if the local variance is high. Our further
investigations found that land-cover objects can also
be grouped into homogeneous and textured land
cover objects which offer better discrimination in
each group. Based on these findings we have
proposed an integrated multi-sensor classification
scheme [1]. The same procedure can be used for
classifying optical or radar input images. We use the
multivariate Gaussian distribution to model the
homogeneous part of an image, and use the
multinomial distribution to model the gray level co-
occurrences of the textured part [9]. We apply a
spectral-based classifier to the homogeneous part
anda texture-based classifier to the textured part of
an image. These classifiers use maximum-likelihood
decision rule which work concurrently on an input
image.
Low-level data fusion may be done to
improve radar image classification accuracy or to
exploit the synergy of multi-sensor information. The
data fusion method may include algebraic
operations, the principal component transformation
or the Karhunen-Loeve transform, FCC or IHS
tranformation, augmented vector classification, and
hierarchical data fusion. We have utilized the
intensity transformation based on the Karhunen-
Loeve transform [11] and hierarchical data fusion
[4]
The classification scheme was discussed in
[1]. Basically, there are three parameters that control
the proposed classifier:
(a) A threshold value that decides if a pixel belongs
to either the homogeneous or the textured region,
(b) Type of each land cover object (homogeneous,
textured, or both), and
(c) The window size over which the texture meaures
are computed.
The threshold value can be tuned so that we
can even have a fully spectralbased classifier or a
fully texture-based classifier if it is necessary. The
type of each land cover object can be determined
based on the labeled training samples. We can use a
window size as small as 3x3 if there are roads or
other line-shaped objects, or a window size of 9x9 if
larger objects are contained in the image.
Several studies have found that the
temporal variations of MODIS vegetation index
(NDVI/EVI) values are related to climatic conditions
such as temperature and precipitation [12-15]. The
authors discovered that the interannual variation in
Normalized Difference Vegetation Index (NDVI)
and Enhanced vegetation index (EVI) values for
specific eight-day periods was correlated with the
phenological indicators [12].It has been
demonstrated that vegetation covers of different
moisture conditions or different species
compositions have different variation patterns in the
time series of the MODIS Enhanced Vegetation
Index (EVI) values[16]. Template matching isa
fundamental method of detecting the presence or
theabsence of objects and identifying them in an
image. Atemplate isitself an image that contains a
feature or an object or a part ofa bigger image, and
isused to search a given image for the presence or
the absence of the contents ofthe template. This
search iscarried out by translating the template
systematically pixel - by-pixel all over the image,
and at each position ofthe template the closeness of
the template to the area covered by it ismeasured.
The locationat which the maximum degree of
closeness isachieved isdeclared to be the location of
the object detected [17].Template matching is one of
the simple techniques used from past many decades.
It is a basic technique for image as it can answer too
many questions related to image [27]. We give the
faster algorithm Sum of Squared Difference on
NDVI values of Remote Sensing data. Also the
intention of this paper is not to say that this measure
opposes the other measures.
While vegetation is the concern, there
should be accuracy in classifying the image based on
proper criteria which must leads to a valid
conclusion. Existing models of the vegetation
dynamic are typically ignores the spatial correlations
[19]. There are many numbers of techniques which
are used for the template matching. It includes
template matching strategy using template trees
growth [20], Comparison based template matching
[21], Digital Image processing [22], Correlation
techniques in Image processing [23]. Multi-date
sequence of the data can be used to quantifying the
time-space structure of vegetation [24]. As an
example, remotely sensed image series of NDVI [25]
and Enhanced VI (EVI) gathered from the different
sensors can be directly used in analysis of the
structural and functional characteristics of land
covers [26]. For the short lead-time applications in
agricultural water management and forest-fire
assessment, a spatiotemporal model that captures
spatial variation patterns in vegetation conditions and
phenology is required. In this paper, we propose a
predictive multidimensional model of vegetation
anomalies that overcomes the limitations of existing
approaches. The model is based on a two-step
process. The first step describes the deterministic
component of vegetation dynamics through an
additive model, including seasonal components,
interannual trends, and jumps. The spatial
dependences are neglected in the first step. The
deterministic model (DM) represents a filtering
procedure able to generate a new stationary time
series of anomalies (residuals). The second step
assumes that the residual component of the DM is a
3. Ms. Neha Bhatt, Mr. IndrJeet Rajput, Mr. Vinitkumar Gupta / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.174-179
176 | P a g e
stochastic process which exhibits systematic
autoregressive (AR) and spatial dependences. Then,
the dynamics of the anomalies are analyzed through a
multidimensional model (space-time AR (STAR)
model), which accounts for the AR characteristics
and the spatial correlations of the remotely sensed
image sequences [18].
III. APPROACH
There are many approaches to classify the
image sensed using Remote Sensing Satellites.
A. Classical Approach
There are many techniques available for
classifying an image. Some of them are listed
below.
• Comparison based template matching
• Digital Image Processing
• Correlation techniques
• Pattern matching
• Time-space structure of vegetation
B. Our Approach
NDVI values can be calculated using the
following algorithm.
For each i in range
ndvi_lower_part = (nir_value[i] + red_value[i])
ndvi_upper_part = (nir_value[i] - red_value[i])
ndvi = ndvi_upper_part/ndvi_lower_part
Algorithm1: Calculate NDVI values
Similarity
Measure
Formula
Sum of
Squared
Difference
(SSD)
Wji
jyixIjiI
),(
2
21 )),(),((
Sum of
Absolute
Difference
(SAD)
Wji
jyixIjiI
),(
21 |),(),(|
Zero-mean
Sum of
Absolute
Difference
(ZSAD)
Wji
jyixIjyixIjiIjiI
),(
2211 |),(),(),(),(|
Locally
scaled
Sum of
Absolute
Difference
(LSAD)
Wji
jyixI
jyixI
jiI
jiI
),(
2
2
1
1 |),(
),(
),(
),(|
Table1: Similarity Measures
Table 1shows some of the similarity measures
available which can be applicable to image data. This
type of similarity measure we called it as correlation
based similarity measure. In this technique, we are
taking window of a small size and the pixels in that
region are compared with the reference of that type
of image and small window of that region. This
method checks the similarity pixel by pixel.
There are many other similarity measures like
Sum of the Squared Difference (SSD), Zero-mean
Sum of the Squared Difference (ZSSD), Locally
Scaled Sum of the Squared Difference (LSSD),
Normalized Cross Correlation (NCC), Zero mean
Normalized Cross Correlation (ZNCC) and Sum of
Hamming Distance (SHD).
Consider the equation shown below:
),(2),(1
)),(2),(1(
jyixIjiI
jyixIjiIAbs
Equation [1]
Above formulae is a proposed formula to measure
similarity. It is based on Sum of Absolute Difference
and it can provide nearer result to the Sum of
Absolute Difference. Now apply above formulae to
Unknown image with Reference image. Images used
for applying above algorithm is as shown below.
Figure 2: 18 Feb 2013, 15 Jan 2013, 10 Dec 2012,
8 Nov 2012, 2 Oct 2012 Respectively
4. Ms. Neha Bhatt, Mr. IndrJeet Rajput, Mr. Vinitkumar Gupta / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.174-179
177 | P a g e
Below is the file used for Reference classes
2/10/12 8/11/12 10/12/12 15/01/13 18/02/13
Ref-
Class
137.89 103.56 102.22 132.44 165.11 1
157.56 106 92.33 127.33 168 2
115.56 93.67 113.89 147.22 155.44 3
133.56 97 105.67 129.44 156.44 4
142.33 96.67 104.78 117.11 162.22 5
96.22 116.44 153.89 158 131.89 6
103.56 108.44 141 156.78 141.11 7
102.56 111.33 161 156.11 129.78 8
100.44 123.11 156.44 154.33 105.78 9
97.56 113.89 167 157 131 10
116.11 111.89 156.89 126.67 136.78 11
71.44 66.22 53 60.33 82.67 12
78.78 76.44 72.67 81.11 72.33 13
79.22 71.67 61 60.89 83.11 14
74.11 75 74.33 77 83.44 15
115.78 102.44 107 115.44 101.33 16
106.33 101.11 100.22 99.56 96.67 17
142.89 120.44 114.56 124.44 104.11 18
160.78 127.44 140 125.89 110.89 19
156.22 119.89 136.78 126.56 107.11 20
Table2: Reference File
Below is the file used to compare unknown class.
2/10/12 8/11/12
10/12/
12
15/01/
13
18/02/
13
Cluster
No
101.58 4.7 119.21 0 111.11 1
1.01 90.72 0 114.33 0 2
70.25 73.96 66.27 78.99 91.33 3
101.08 97.52 99.38 99.32 96.68 4
98.02 98.76 107.31 114.17 114 5
Table3: Cluster File
Proposed Work:
1. Take 5 different dates Reference and
Unknown cluster images.
2. Calculate NDVI images for all 5 images
using algorithm 1 as shown above.
3. Get the Unknown interested regions from
the image and collect the NDVI values into
1 file.
4. Normalize NDVI values of -1 to 1 into 0 to
200 by
using formulae:
ndvi_new = ndvi_old x 100 + 100
5. Make the Reference file and Unknown
cluster file as shown above in Table 2 and
Table 3.
6. Apply Equation [1] given above for these 2
files and get the class label.
7. Get the output as shown in Table4. It will
contain the class in which unknown class is
classified.
IV. IMPLEMENTATION
To calculate NDVI values, we can use
GDAL library. GDAL is one type of Translator
library, generally used for raster Geospatial data
format and which is open source. We will use
Python language for programming and we can use
Eclipse IDE. We can implement the functionality
shown in Algorithm1 in Python after embedding
GDAL library into it. After calculating NDVI
values, we can use this NDVI data to apply
Equation [1]. Here, we will use .Net technology
with C# as programming language to implement
Equation1. Fig. 4 shows the design of the
implementation of algorithm. It will take input as
Reference File and Cluster File and it will produce
Output file which will contain classification
details.
159.74 130.6 147.48 136.85 120.94 19
1.01 90.72 0 114.33 0 1
70.25 73.96 66.27 78.99 91.33 15
101.08 97.52 99.38 99.32 96.68 17
98.02 98.76 107.31 114.17 114 16
Table4: Output File
After classification process when we can
compare the resulted Reference and unknown
cluster it can be shown as a graph.
Figure 3: Analysis
90
92
94
96
98
100
102
104
106
108
Cluster
Reference
5. Ms. Neha Bhatt, Mr. IndrJeet Rajput, Mr. Vinitkumar Gupta / International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.174-179
178 | P a g e
V. CONCLUSION
From above implementation results, we can
conclude that this algorithm is much faster
compared to other approaches as it includes small
mathematical calculation and it take O (n) time to
compute the result. The „n‟ value depends upon
number of clusters that needs to classify.
VI. FUTURE WORK
This algorithm can also used to enhance the
perfection by including Standard deviation. Also it
can‟t be used for needs where some dates are having
more importance and need to give more preference to
that particular date. In that case, we need to modify
this algorithm to some point. This feature can also be
implemented using Sum of Absolute Difference. In
that case, we can get similar value to that of Sum of
Squared Difference.
ACKNOWLEDGMENT
We would like to thank the School of Computer
Science and Engineering, GTU, for giving us such an
opportunity to carry out this research work and also
for providing us the requisite resources and
infrastructure for carrying out the research.
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