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.
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
A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
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
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
A Review of Change Detection Techniques of LandCover Using Remote Sensing Dataiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
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
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.
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.
Remote Sensing and GIS provide a solid geospatial data foundation, which is suitable for addressing questions on choosing the best sites of dams. To accomplish this, there is a need to understand geospatial processes of gathering, organizing and analyzing data using Remote Sensing (RS) and GIS. On the other hand, theoretical and practical backgrounds are needed to understand linkages, relationships and thresholds that allow faster identification of the study area. DEMs are essential for topographic characterization by representing land surface, hydrological boundaries and terrain attributes, such as slope and aspect. In this work it has been studied for the generation of Digital Elevation Model (DEM) and the resulted DEM is used to generate the Orthoimage and to demark the land use/ land cover features and also for drainage pattern for the catchment area of hydro project.
Using Remote Sensing Techniques For Monitoring Ecological Changes In Lakes: C...IJERA Editor
The ability to use remote sensing in studying lake ecology lies in the capability of satellite sensors to measure
the spectral reflectance of constituents in water bodies. This reflectance can be used to determine the
concentration of the constituents of the water column through mathematical relationships. This work identified a
simple linear equation for estimating suspended matter in Lake Naivasha with reflectance in Landsat7 ETM+
image. A R² = 0.94, n = 6 for suspended matter was obtained. Archive of Landsat imagery was used to
produce maps of suspended matter concentrations in the lake. The suspended matter concentrations at five
different locations in the lake over 30 year’s period were then estimated. It was therefore concluded that the
ecological changes Lake Naivasha is experiencing is the result of the high water abstraction and the effect of
climate change.
Using Remote Sensing Techniques For Monitoring Ecological Changes In Lakes: C...IJERA Editor
The ability to use remote sensing in studying lake ecology lies in the capability of satellite sensors to measure
the spectral reflectance of constituents in water bodies. This reflectance can be used to determine the
concentration of the constituents of the water column through mathematical relationships. This work identified a
simple linear equation for estimating suspended matter in Lake Naivasha with reflectance in Landsat7 ETM+
image. A R² = 0.94, n = 6 for suspended matter was obtained. Archive of Landsat imagery was used to
produce maps of suspended matter concentrations in the lake. The suspended matter concentrations at five
different locations in the lake over 30 year’s period were then estimated. It was therefore concluded that the
ecological changes Lake Naivasha is experiencing is the result of the high water abstraction and the effect of
climate change.
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.
Effectiveness and Capability of Remote Sensing (RS) and Geographic Informatio...nitinrane33
In this research paper, the effectiveness and capability of remote sensing (RS) and geographic information systems (GIS) are investigated as powerful tools for analyzing changes in land use and land cover (LULC), as well as for accuracy assessment. The study employs the literature of satellite imagery and GIS data to evaluate LULC changes over a period and to assess the accuracy of the analysis. Moreover, the research investigates the land use and land cover change detection analysis using RS and GIS, application of artificial intelligence (AI), and Machine Learning (ML) in LULC classification, environment and risk evaluation, stages of process LULC classification, factors affecting the LULC classification, accuracy assessment, and potential applications of RS and GIS in predicting future LULC changes and supporting decision-making processes. The findings of the study suggest that RS and GIS are highly effective and accurate for LULC analysis and assessment, with substantial potential for predicting and managing future changes in land use and land cover. The paper emphasizes the importance of utilizing RS and GIS techniques in the field of sustainable environmental management and resource planning.
Topographic Information System as a Tool for Environmental Management, a Case...iosrjce
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online.
Land use/land cover classification using machine learning modelsIJECEIAES
An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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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.
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.
Remote Sensing and GIS provide a solid geospatial data foundation, which is suitable for addressing questions on choosing the best sites of dams. To accomplish this, there is a need to understand geospatial processes of gathering, organizing and analyzing data using Remote Sensing (RS) and GIS. On the other hand, theoretical and practical backgrounds are needed to understand linkages, relationships and thresholds that allow faster identification of the study area. DEMs are essential for topographic characterization by representing land surface, hydrological boundaries and terrain attributes, such as slope and aspect. In this work it has been studied for the generation of Digital Elevation Model (DEM) and the resulted DEM is used to generate the Orthoimage and to demark the land use/ land cover features and also for drainage pattern for the catchment area of hydro project.
Using Remote Sensing Techniques For Monitoring Ecological Changes In Lakes: C...IJERA Editor
The ability to use remote sensing in studying lake ecology lies in the capability of satellite sensors to measure
the spectral reflectance of constituents in water bodies. This reflectance can be used to determine the
concentration of the constituents of the water column through mathematical relationships. This work identified a
simple linear equation for estimating suspended matter in Lake Naivasha with reflectance in Landsat7 ETM+
image. A R² = 0.94, n = 6 for suspended matter was obtained. Archive of Landsat imagery was used to
produce maps of suspended matter concentrations in the lake. The suspended matter concentrations at five
different locations in the lake over 30 year’s period were then estimated. It was therefore concluded that the
ecological changes Lake Naivasha is experiencing is the result of the high water abstraction and the effect of
climate change.
Using Remote Sensing Techniques For Monitoring Ecological Changes In Lakes: C...IJERA Editor
The ability to use remote sensing in studying lake ecology lies in the capability of satellite sensors to measure
the spectral reflectance of constituents in water bodies. This reflectance can be used to determine the
concentration of the constituents of the water column through mathematical relationships. This work identified a
simple linear equation for estimating suspended matter in Lake Naivasha with reflectance in Landsat7 ETM+
image. A R² = 0.94, n = 6 for suspended matter was obtained. Archive of Landsat imagery was used to
produce maps of suspended matter concentrations in the lake. The suspended matter concentrations at five
different locations in the lake over 30 year’s period were then estimated. It was therefore concluded that the
ecological changes Lake Naivasha is experiencing is the result of the high water abstraction and the effect of
climate change.
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.
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In this research paper, the effectiveness and capability of remote sensing (RS) and geographic information systems (GIS) are investigated as powerful tools for analyzing changes in land use and land cover (LULC), as well as for accuracy assessment. The study employs the literature of satellite imagery and GIS data to evaluate LULC changes over a period and to assess the accuracy of the analysis. Moreover, the research investigates the land use and land cover change detection analysis using RS and GIS, application of artificial intelligence (AI), and Machine Learning (ML) in LULC classification, environment and risk evaluation, stages of process LULC classification, factors affecting the LULC classification, accuracy assessment, and potential applications of RS and GIS in predicting future LULC changes and supporting decision-making processes. The findings of the study suggest that RS and GIS are highly effective and accurate for LULC analysis and assessment, with substantial potential for predicting and managing future changes in land use and land cover. The paper emphasizes the importance of utilizing RS and GIS techniques in the field of sustainable environmental management and resource planning.
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An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
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The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
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significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Performance analysis of change detection techniques for land use land cover
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4339~4346
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4339-4346 4339
Journal homepage: http://ijece.iaescore.com
Performance analysis of change detection techniques for land
use land cover
Aarti Karandikar, Avinash Agrawal
Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Article Info ABSTRACT
Article history:
Received Jun 24, 2022
Revised Sep 26, 2022
Accepted Oct 1, 2022
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.
Keywords:
Change detection
Deep learning
Land use land cover
Post classification
Remote sensing
This is an open access article under the CC BY-SA license.
Corresponding Author:
Aarti Karandikar
Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and
Management
Nagpur, India
Email: karandikara@rknec.edu
1. INTRODUCTION
The satellites and unmanned aerial vehicles are fast becoming a huge data source. This has paved
the way for use of remote sensing images to detect changes on the earth’s surface. Change pertaining to the
surface of earth have become important for monitoring the local, regional and global resources and
environment. Change detection (CD) has been defined in [1] as “the process of identifying differences in the
state of an object or phenomena by observing it at different times”. In other words, change detection is the
process of finding regions that have undergone spatial or spectral modifications and the reasons behind it. A
change map is constituted from the images captured at different period of time.
Change detection techniques provide valuable information of the possible transformations a given
scene has suffered over time. Change detection is complicated by the fact that change can occur in the
temporal and/or spectral domains [2]. Changes can be due to: a biological action in nature, biological action,
and human activity. As human and natural forces continue to alter the landscape, various public and private
agencies are finding it increasingly important to develop monitoring methods to assess these changes.
Change detection can be used to measure five different types of change [3]: change in the identity of a feature
over time, change of a feature’s shape over time, change of a feature’s location over time, change in a
feature’s size over time, and change in the identify of a feature over time. Gong et al. [4] have
characterized change detection approaches into two broad groups: bi-temporal CD which measures
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4339-4346
4340
changes based on a ‘two-epoch’ timescale and temporal CD that analyses the changes based on a
‘continuous’ timescale.
2. NEED AND IMPORTANCE OF CHANGE DETECTION
Availability of satellite images has given rise to the use of these images in monitoring the changes
occurring on the surface of the earth. Timely and accurate analysis of the detected changes play an important
role in understanding natural phenomenon and changes occurring due to these. It is also used to understand
the impact of anthropogenic activities on the environment. The foremost aim of change detection method is
to identify significant changes occurring at the same location over a period of time. These changes are
captured in a series of images by a satellite. Popular satellite data for remote sensing applications are Landsat
multispectral scanner (MSS), thematic mapper (TM), SPOT, and MODIS. Major steps involved in the change
detection process are [5]: image pre-processing, selection of suitable techniques, and accuracy assessment.
Figure 1 shows the framework of change detection [6]. The change detection has got its various applications
few of them are as follows: deforestation, crop monitoring, moisture content of soil, urban planning, and
water quality.
Figure 1. Change detection framework
3. REVIEW OF CHANGE DETECTION TECHNIQUES
The Earth’s surface marks the presence of different types of landscapes. The selection of proper
change detection tool is important to analyze changes in these land forms. Image pre-processing plays a
major role in the outcome change detection process. Depending on the application, there are many
approaches for change detection of satellite images [7]. Figure 2 shows the different change detection
methods. A comparative analysis of four of the most commonly used change detection methods namely:
i) transformation-based CD, ii) classification-based CD, iii) artificial neural network (ANN) based CD, and
iv) advanced models of CD is presented in this study.
Figure 2. Change detection methods
3.1. Transformation based CD
Change detection using pixel transformation for detecting the measure of change in the images has
been extensively studied in the literature. As mentioned in Table 1, these methods are: principal component
analysis (PCA) [8]–[10], a variant of PCA called Taselled Cap or Kauth-Thomas (KT) transformation [11],
[12], Gramm-Schmidt transformation and Chi-Square Transform [13]. Table 1 gives details regarding the
different transformation-based methods and the application areas in which these methods were used. Out of
all the methods mentioned in the literature, PCA is the most frequently used approach for detecting change or
no-change information.
3. Int J Elec & Comp Eng ISSN: 2088-8708
Performance analysis of change detection techniques for land use land cover (Aarti Karandikar)
4341
Principal component analysis is a transformation-based change detection technique. It is a
dimensionality reduction method in which principal components are computed by performing a change of
basis. The data in the direction of maximum variance is retained. The reduced features are uncorrelated with
each other. PCA based land use change detection technique was used in [8] to identify land use changes in
the Hangzhou City from 2000 to 2003. PCA was used to enhance the change information in the Landsat
images. A hybrid classifier gave improved accuracy. Based on principal component analysis [9] proposed a
framework for detecting changes in multidimensional data streams. Their method reduces computational
costs by using a density estimator. The efforts required to minimize threshold setting is reduced through the
use of Page-Hinkley test. Chakraborty [14] used MODIS Terra images to detect change in forest areas of the
Barak Basin of north-eastern India that covers the states of Assam, Manipur, Mizoram, Nagaland and
Tripura. PCA was applied on enhanced vegetation index (EVI) composite images of 2000 to 2006. The forest
change map was used to identify hotspots or areas of high disturbance. Robust PCA (RPCA) via principal
component pursuit (PCP) was used in [15] for change detection in ultrawideband very high-frequency
synthetic aperture radar (SAR) images of CARABAS-II data set. RPCA refers to the problem of PCA when
the data may be corrupted by outliers [16]. The main drawback of this approach is, it is difficult to label the
changed area in an image.
Table 1. Detailed survey of transformation-based change detection method
Author Specific method Dataset Application area
Fung and Ledrew [8] PCA Landsat Land cover change detection
Gong [9] PCA Landsat Land cover change detection
Deng et al. [10] PCA Spot-5 Landsat City expansion
Solano-Correa et al. [11] Tasseled Cap Transformation Landsat Land cover change detection
Thakkar et al. [12] Tasseled Cap Transformation Landsat Land cover change detection
Vazquez-Jimenez et al. [13] Chi-square Quickbird Land cover change detection
Chakraborty [14] PCA MODIS Forest change detection
Schwartz et al. [15] RPCA CARABAS-II Land cover change detection
3.2. Classification based CD
This approach is entirely dependent on the choice of data for change analysis. The methods are
divided into pre-classification and post-classification. The pre-classification approach is mostly used for
change and no-change, rate of change, and image enhancement, while the post-classification is mostly used
for “from-to” change analysis and comparison of individually classified images. Table 2 presents a detail
study of different classification-based methods along with the application area in which they were used. From
the study, it was found that post-classification method was the most used in classification-based change
detection. The pre-classification approach is used in [17] for image enhancement, change and no-change, and
change rate, while the post-classification is mostly used for “from-to” change analysis and comparison of
individually classified images. Afify [18] has compared image differencing, post-classification, principal
component analysis, and image rationing techniques to monitor and assess the extent of land cover changes
in the city of Burg El-Arab, Egypt. Among these four techniques, the post classification change detection
technique provided the highest accuracy followed by the image rationing (IR) and image differencing (ID)
techniques while the PCA technique gave the least accuracy. Urban land cover change of Hurghada in Egypt
was evaluated by [19]. Of the five change detection techniques applied, post-classification method was found
to be the most suitable and accurate method. Hossen et al. [20] used unsupervised iso-data clustering,
Mahalanobis distance, maximum likelihood supervised classification, normalized difference water index, and
minimum distance supervised classification to evaluate and predict future changes in Manzala Lake, Egypt.
Maximum likelihood classifier (MLC) achieved highest overall accuracy of 93.33% in comparison to other
techniques. To predict future changes, linear regression was used. Supervised classification technique
maximum likelihood algorithm was used to determine changes in the Kupti watershed of Darwha block,
Maharashtra, India over the period of 15 years from 2000 to 2016 [21]. Classes demarcated on the basis of
supervised classification: agriculture, forest cover, wasteland, habitation, and waterbody. Historical data from
Corona dataset was mapped with Landsat data and changes in the forest areas of Virginia-Maryland, United
States and Mato Grosso-Tocantins-Pará, Brazil were studied by [22]. Corona images were used to detect
changes in the year 1960 and Landsat images were used for the period 1980 to 2000. Forest changes were
mapped using the support vector machine (SVM) algorithm [23].
SVM was used by [24] to analyze built-up and non-built-up changes in Landsat images of Harare
Metropolitan Province, Zimbabwe. Halmy et al. [25] mapped the land use/land cover (LULC) distribution of
the north-western desert of Egypt to study land use/land cover changes of the desert landscape for 1988,
1999, and 2011. A random forest approach was used to produce the LULC maps with more than 90%
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4339-4346
4342
accuracy. From their study they found that depending upon the land use, the study area was subjected to
different types of modifications. Markov-CA was used to project changes in 2023 by extrapolating current
trends.
Pre-classification and post-classification change detection techniques were used in [26] on Tanguar
Haor, Bangladesh images to analyze changes from 1980 to 2010 in. In pre-classification approach: change
vector analysis, normalized difference vegetation index, and normalized difference water index (NDWI)
analysis were implemented to assess the change scenario. Maximum likelihood classification technique was
used to categorize land cover into shallow water, deepwater, vegetation, and settlement. ENVI thematic
change workflow tool was used as a post classification tool. Combination of these techniques helped to
understand the direction, dynamics, state, and magnitude of change.
Bitemporal change detection to determine the urban growth of Madurai, India with wavelet-based
post classification change detection technique on two MSS land cover images of 1996 and 2004 was explored
in [27]. Texture feature vector was given as input to a fuzzy c-means classifier to identify the urban growth of
the city. The accuracy of the change map was assessed using error matrix analysis which showed the
superiority of this method as compared to change vector analysis, image differencing, and PCA.
Vignesh et al. [28] grouped images into clusters and used them as training sets for an unsupervised
classification algorithm ensemble minimization learning algorithm (EML) for land cover classification. This
algorithm can classify different vegetation types. A disadvantage is it requires some improvement in
classification accuracy.
SVM was used by [24] to analyze built-up and non-built-up changes in Landsat images of Harare
Metropolitan Province, Zimbabwe. Halmy et al. [25] mapped the LULC distribution of the north-western
desert of Egypt to study land use/land cover changes of the desert landscape for 1988, 1999, and 2011. A
random forest approach was used to produce the LULC maps with more than 90% accuracy. From their study
they found that depending upon the land use, the study area was subjected to different types of modifications.
Markov-CA was used to project changes in 2023 by extrapolating current trends.
Pre-classification and post-classification change detection techniques were used in [26] on Tanguar
Haor, Bangladesh images to analyze changes from 1980 to 2010 in. In pre-classification approach: change
vector analysis, normalized difference vegetation index, and Normalized Difference Water Index (NDWI)
analysis were implemented to assess the change scenario. Maximum likelihood classification technique was
used to categorize land cover into shallow water, deepwater, vegetation, and settlement. ENVI thematic
change workflow tool was used as a post classification tool. Combination of these techniques helped to
understand the direction, dynamics, state, and magnitude of change.
Bitemporal change detection to determine the urban growth of Madurai, India with wavelet-based
post classification change detection technique on two MSS land cover images of 1996 and 2004 was explored
in [27]. Texture feature vector was given as input to a fuzzy c-means classifier to identify the urban growth of
the city. The accuracy of the change map was assessed using error matrix analysis which showed the
superiority of this method as compared to change vector analysis, image differencing, and PCA.
Vignesh et al. [28] grouped images into clusters and used them as training sets for an unsupervised
classification algorithm ensemble minimization learning algorithm (EML) for land cover classification. This
algorithm can classify different vegetation types. A disadvantage is it requires some improvement in
classification accuracy.
Table 2. Detailed survey of classification-based change detection method
Author Specific Method Dataset Application area
Afify [18] Post classification Landsat Urban change detection
Kamh et al. [19] Post classification Landsat Urban growth
Hossen et al. [20] MLC,
Linear regression
Landsat Future land cover prediction
Patangray et al. [21] maximum likelihood algorithm Landsat-4
Google Image
Landsat-8
Analyzing changes in the watershed
area
Song et al. [22] SVM Corona
Landsat-5
Landsat-7
Forest cover change analysis
Huang et al. [23] SVM Landsat Forest cover change analysis
Kamusoko et al. [24] SVM Landsat Urban growth
Halmy et al. [25] Random forest Landsat Desertification
Haque and Basak [26] Pre-classification
Post-classification
Landsat Landscape change over decades
Raja et al. [27] Wavelet-based post classification Landsat Urban expansion
Vignesh et al. [28] Ensemble Minimization Learning algorithm Landsat Rural and urban change detection
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3.3. Artificial neural networks-based CD
The use of artificial intelligence for satellite image processing has increased in recent years. One of
the earlier mentions of the use of artificial neural networks (ANN) for multi-temporal change analysis is
found in [29]. Bi-temporal comparison of two images of Wilmington, North Carolina were acquired of
Landsat TM. A backpropagation training algorithm with four layers was used to detect land changes. Final
classes were: forest, agriculture or bare or urban, cypress or wet deciduous or marsh, and water. The ANN
model had an overall accuracy of 95.6% for four class classification schemes whereas the maximum
likelihood classifier gave an accuracy of 86.5%. In [30] ANN was used to perform vegetation change
detection on two images of 2003 and 2004. The results were compared with the post-classification method. It
was observed that combining NDVI differencing method with visual interpretation gives better results. Fkirin
[31] used two datasets and trained the neural network to detect changes using an improvement factor. Not
just change detection, but changes in classes like vegetation to water, and desert to vegetation were also
detected.
3.4. Advanced models of CD
In recent times, convolution neural networks and recurrent neural networks have been employed in
the study of change detection. A detailed study of the use of artificial neural networks and advanced neural
models for change analysis is presented in Table 3. Morgan et al. [32] used the U-net convolutional neural
network (CNN) classification algorithm. Results of change in bi-temporal high-resolution images were
compared with random trees and support vector machine algorithms. Comparisons showed that U-Net
classifier had an overall accuracy of 92.4% as opposed to SVM with 81.6% and RT with 75.7%.
Ahangarha et al. [33] trained the U-net CNN model for generating change maps of Hong Kong city
images from the Onera satellite change detection (OSCD) dataset. This dataset consists of images captured
using the Sentinel-2 satellite. Overall accuracy was 95% and value of Kappa was close to one. The use of a
deep belief network for image differencing was studied in [34]. An increase in the difference between
changed area and a decrease in the not changed area was achieved by tuning the deep belief algorithm
through a modified backpropagation algorithm. Change detection results are generated through clustering
analysis of difference images. In [35] CNN was used for semantic segmentation. Their model was able to
locate places of change in given input images. Zhang and Lu [36] have proposed spectral-spatial joint
learning network (SSJLN) that contains three parts: spectral-spatial joint representation, feature fusion, and
discrimination learning. They evaluated the performance of their proposed method on four datasets. Other
extensions of CNN are also studied. Mou et al. [37] has used a combination of convolutional neural network
and recurrent neural network, Karandikar [38] have proposed a pixel-based method that uses differencing and
LSTM as feature fusion, and [39] implemented convolutional neural network under an object-based image
analysis framework. Pomente et al. [40] pretrained the data with sufficient labeled samples in other domain
data and used it in the deep feature learning phase of multilevel convolutional neural network. Zhu et al. [41]
used SegNet, Venugopal [42] used deep lab dilated convolutional neural network (DL-DCNN), Varghese
[43] used ChangeNet, while in [44] Hopfield neural network was used. In most cases, freely available
Landsat [45] data is used. [46] discusses the pros and cons of using artificial intelligence in remote sensing.
Recent studies show the use of advanced models of deep neural networks can improve accuracy of change
detection [47]–[50]. Use of CNN and RNN has changed the way digital remotely sensed images are
processed
Table 3. Detailed survey of artificial neural network and advanced models of change detection method
Author Specific Method Dataset Application Area
Dai and Khorram [29] ANN Landsat Land change analysis
Zang et al. [30] ANN Landsat Vegetation change detection
Morgan et al. [32] U-Net NAIP Coastal marsh change detection
Ahangarha et al. [33] U-Net CNN Onera Satellite
CD
Environmental change detection
Chu et al. [34] Deep Belief Networks -- Land change analysis
Jong and Bosman [35] CNN Vaihingen Dataset Land change analysis
Liu et al. [39] CNN Opendata Land change analysis
Zhu et al. [41] SegNet -- Land change analysis
Varghese et al. [43] ChangeNet VL-CMU-CD
Tsunami
GSV
Visual change detection
Ghosh et al. [44] Hopfield type neural
network
SOM based neural network
Landsat-5
Landsat-7
Urban change analysis
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4. CONCLUSION
New algorithms and methods are developed to overcome the drawbacks of the existing algorithms.
For case of remotely sensed data, there are many aspects which govern the outcome of any change detection
algorithm. Some common factors identified from the literature are difficulty in image acquisition, noise, pre-
processing of images, size of images, and computational complexity. Complexity of image pre-processing
increases if data is captured from different sources. Due to the varying nature of the data collected, there is no
single technique which is applicable on all types of satellite images. Careful consideration of application area
is required while selecting a change detection method. Another important factor is the source of satellite data.
The paper has discussed majorly used methods of change detection found in literature. In
transformation-based methods, principal component analysis was found to be the most popular. A main
disadvantage of this method is the difficulty of interpreting and labelling change data on the transformed
images. In classification-based methods, post classification and maximum likelihood classifier are the
commonly used techniques. Although classification-based change detection methods are the common choice
for detecting changes, it is tedious and time consuming to select training samples. This affects the
classification accuracy and as a result change detection is unsatisfactory. From the past few years, many
researchers have applied artificial intelligence techniques in change detection. However, there is still no
efficient way to design and train the neural network and it is still an enduring issue in the field of remote
sensing. In view of all this, we conclude that a hybrid changes detection framework comprising of individual
change detection technique improves the overall accuracy
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BIOGRAPHIES OF AUTHORS
Aarti Karandikar is assistant professor in the Department of Computer Science, RCOEM,
Nagpur. She has completed her graduation in B.E. (CSE) from Amravati University and post-graduation
in M.Tech. (CSE) from Nagpur University. She is currently pursuing her Ph.D. from RTMNU, Nagpur,
India. Her area of research are remote sensing and data analytics. She can be contacted at email:
karandikara@rknec.edu.
Avinash Agrawal is associate professor and Head of Department of Computer Science and
Engineering at RCOEM, Nagpur. He has done his BE from Nagpur university, M.Tech. in CSE from NIT
Raipur, and has completed hid Ph.D from VNIT Nagpur. His research interests are natural language
processing, data mining and artificial intelligence. He has more than 70 publications in reputed journals
and conferences. He can be contacted at email: agrawalaj@rknec.edu.