The Experts below are selected from a list of 3189 Experts worldwide ranked by ideXlab platform
Ujjwal Maulik - One of the best experts on this subject based on the ideXlab platform.
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TENCON - Improved Fuzzy Clustering using Ensemble based Differential Evolution for Remote Sensing Image
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2019Co-Authors: Jnanendra Prasad Sarkar, Indrajit Saha, Ujjwal MaulikAbstract:Identification of homogeneous regions in a Satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like Satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) Satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.
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ICAPR - Unsupervised Satellite Image Segmentation by Combining SA Based Fuzzy Clustering with Support Vector Machine
2009 Seventh International Conference on Advances in Pattern Recognition, 2009Co-Authors: Anirban Mukhopadhyay, Ujjwal MaulikAbstract:Fuzzy clustering is an important tool for unsupervised pixel classification in remotely sensed Satellite images. In this article, a Simulated Annealing (SA) based fuzzy clustering method is developed and combined with popular Support vector Machine (SVM) classifier to fine tune the clustering produced by SA for obtaining an improved clustering performance. The performance of the proposed technique has been compared with that of some other well-known algorithms for an IRS Satellite image of the city of Kolkata and its superiority has been demonstrated quantitatively and visually.
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combining multiobjective fuzzy clustering and probabilistic ann classifier for unsupervised pattern classification application to Satellite image segmentation
World Congress on Computational Intelligence, 2008Co-Authors: Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Ujjwal MaulikAbstract:An important approach to unsupervised pixel classification in remote sensing Satellite imagery is to use clustering in the spectral domain. In this article, a recently proposed multiobjective fuzzy clustering scheme has been combined with artificial neural networks (ANN) based probabilistic classifier to yield better performance. The multiobjective technique is fIRSt used to produce a set of non-dominated solutions. A part of these solutions having high confidence level are then used to train the ANN classifier. Finally the remaining solutions are classified using the trained classifier. The performance of this technique has been compared with that of some other well- known algorithms for two artificial data sets and a IRS Satellite image of the city of Calcutta.
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IEEE Congress on Evolutionary Computation - Combining multiobjective fuzzy clustering and probabilistic ANN classifier for unsupervised pattern classification: Application to Satellite image segmentation
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Anirban Mukhopadhyay, S. Bandyopadhyay, Ujjwal MaulikAbstract:An important approach to unsupervised pixel classification in remote sensing Satellite imagery is to use clustering in the spectral domain. In this article, a recently proposed multiobjective fuzzy clustering scheme has been combined with artificial neural networks (ANN) based probabilistic classifier to yield better performance. The multiobjective technique is fIRSt used to produce a set of non-dominated solutions. A part of these solutions having high confidence level are then used to train the ANN classifier. Finally the remaining solutions are classified using the trained classifier. The performance of this technique has been compared with that of some other well- known algorithms for two artificial data sets and a IRS Satellite image of the city of Calcutta.
Praveen Kumar - One of the best experts on this subject based on the ideXlab platform.
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extracting seasonal cropping patterns using multi temporal vegetation indices from IRS liss iii data in muzaffarpur district of bihar india
The Egyptian Journal of Remote Sensing and Space Science, 2014Co-Authors: Saptarshi Mondal, C Jeganathan, Nitish Kumar Sinha, Harshit Rajan, Tanmoy Roy, Praveen KumarAbstract:Abstract The advancement in Satellite technology in terms of spatial, temporal, spectral and radiometric resolutions leads, successfully, to more specific and intensified research on agriculture. Automatic assessment of spatio-temporal cropping pattern and extent at multi-scale (community level, regional level and global level) has been a challenge to researchers. This study aims to develop a semi-automated approach using Indian Remote Sensing (IRS) Satellite data and associated vegetation indices to extract annual cropping pattern in Muzaffarpur district of Bihar, India at a fine scale (1:50,000). Three vegetation indices (VIs) – NDVI, EVI2 and NDSBVI, were calculated using three seasonal (Kharif, Rabi and Zaid) IRS Resourcesat 2 LISS-III images. Threshold reference values for vegetation and non-vegetation thematic classes were extracted based on 40 training samples over each of the seasonal VI. Using these estimated value range a decision tree was established to classify three seasonal VI stack images which reveals seven different cropping patterns and plantation. In addition, a digitised reference map was also generated from multi-seasonal LISS-III images to check the accuracy of the semi-automatically extracted VI based classified image. The overall accuracies of 86.08%, 83.1% and 83.3% were achieved between reference map and NDVI, EVI2 and NDSBVI, respectively. Plantation was successfully identified in all cases with 96% (NDVI), 95% (EVI2) and 91% (NDSBVI) accuracy.
Sanjay K. Jain - One of the best experts on this subject based on the ideXlab platform.
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Using Remote Sensing Data to Study Wetland Dynamics - A Case Study of Harike Wetland
2008Co-Authors: Archana Sarkar, Sanjay K. JainAbstract:In the context of environment, wetlands play a very important role. They protect and improve the quality of water and keep the local weather moderate. They are one of the most productive areas of our ecological system. Disappearance of such wetlands has caused changes in weather conditions and reduction of sub-soil water level. The Harike wetland ecosystem in the Punjab state of India was created in 1953 by the construction of a barrage at the confluence of the Satluj and Beas rivers. Considered a wetland of international importance, it was included in the list of Ramsar sites in 1990. It is a breeding ground and habitat for a large variety of migratory as well as domiciled birds. Like many wetlands in India, Harike is also beset with problems and its area is reducing. Therefore, in the present study, the wetland area mapping has been carried out using multidate IRS Satellite data and the wetland area has been classified into aquatic vegetation, water spread area and water logged area for the years 1990, 1999 and 2003. These water related features have been delineated using normalized difference water index (NDWI). It has been found that the wetland area has reduced by more than 30% over a period of 13 years.
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SLURP model and GIS for estimation of runoff in a part of Satluj catchment, India
Hydrological Sciences Journal, 1998Co-Authors: Sanjay K. Jain, Naresh Kumar, Tanvear Ahmad, G. W. KiteAbstract:Abstract The snow and rain in the Himalayas are the main sources of supply for the rivers in the Indo-Gangetic plains. Irrigation, hydropower generation, and water supply are very much dependent on the availability of water in the Himalaya rivers. Mathematical models serve as important aids for the estimation of water availability in rivers. In the present study the SLURP watershed model is applied to a rainfed area of the Satluj catchment located in the western Himalayas, India. The SLURP model developed at NHRI, Canada, is a distributed conceptual model which simulates the behaviour of a watershed by carrying out vertical water balances for each element of a matrix of landcovers and subareas of a watershed and then routing the resulting runoff between subareas. The ILWIS geographic information system was used to prepare the input data required for SLURP and land use data were obtained from the IRS Satellite LISS II visible and near infrared sensors. The simulated flows at the Bhakhra Dam outlet of the S...
B.p. Singh - One of the best experts on this subject based on the ideXlab platform.
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An improved high-resolution hybrid stepper motor for solar-array drive of Indian remote-sensing Satellite
IEEE Transactions on Industry Applications, 1997Co-Authors: Karthikeyan Rajagopal, M. Krishnaswamy, Bhim Singh, B.p. SinghAbstract:This paper presents the computer-aided design and development of an improved 720-steps hybrid stepper motor used as the drive motor for the solar array of the Indian remote-sensing (IRS) Satellite in the polar Sun-synchronous orbit. The motor is of pancake type with coil redundancy, and the step angle is 0.5/spl deg/. It is designed to deliver a constant holding torque of 1 Nm against a varying DC supply voltage of 28-42 V and in an operating temperature range from -10/spl deg/C to +60/spl deg/C. The authors introduce a phenomenon named as "torque saturation", achievable in a hybrid stepper motor by properly choosing the operating point of the rotor permanent magnet and the stator winding configuration. Apart from the computer-aided design procedure, relevant details regarding fabrication and testing are also provided. The test results of the developed motor match fairly well with the computed values and confirm the high performance of the developed hybrid stepper motor.
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High resolution hybrid stepper motor with pole redundancy for space application
1993Co-Authors: Karthikeyan Rajagopal, M. Krishnaswamy, Bhim Singh, B.p. Singh, C.m. BhatiaAbstract:This paper describes the design and development of a 720 steps hybrid stepper motor with pole redundancy used as the drive motor for the solar cell array of the Indian Remote Sensing (IRS) Satellite. The motor is of pancake design and the step angle is 0.5 degrees . The holding torque is 0.5 Nm. Apart from the step-by-step design procedure, relevant details regarding fabrication and testing are reported. The test results are quite satisfactory and conform the high performance of the developed motor.
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An improved high resolution hybrid stepper motor for solar array drive of Indian Remote Sensing Satellite
Proceedings of 1995 International Conference on Power Electronics and Drive Systems. PEDS 95, 1Co-Authors: Karthikeyan Rajagopal, M. Krishnaswamy, Bhim Singh, B.p. SinghAbstract:This paper presents the computer-aided design and development of an improved 720 steps hybrid stepper motor used as the drive motor for the solar array of the Indian Remote Sensing (IRS) Satellite in the polar sun-synchronous orbit. The motor is of pancake type with coil redundancy and the step angle is 0.5/spl deg/. It is designed to deliver a constant holding torque of 1 Nm against a varying DC supply voltage of 28 to 42 V and in an operating temperature range from -10 to +60/spl deg/ C. The authors introduce a phenomenon named as "torque saturation" achievable in a hybrid stepper motor by properly choosing the operating point of the rotor permanent magnet and the stator winding configuration. Apart from the computer aided design procedure, relevant details regarding fabrication and testing are also provided. The test results of the developed motor match fairly well with the computed values and confirm the high performance of the developed hybrid stepper motor. >
Saro Lee - One of the best experts on this subject based on the ideXlab platform.
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Landslide susceptibility mapping using GIS and the weight-of-evidence model
International Journal of Geographical Information Science, 2004Co-Authors: Saro Lee, Jaewon ChoiAbstract:The weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial database containing information such as topography, soil, forest, geology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factor's rating for the Boun area in Korea, which had suffered substantial landslide damage following heavy rain in 1998. In the topographic database, the factors were slope, aspect and curvature; in the soil database, they were soil texture, soil material, soil drainage, soil effective thickness and topographic type; in the forest map, they were forest type, timber diameter, timber age and forest density; lithology was derived from the geological database; land-use information came from Landsat TM Satellite imagery; and lineament data from IRS Satellite imagery. Tests of conditional independence were performed for the selection of factors, allowing 43 combinations of...
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Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS
Environmental Management, 2004Co-Authors: Saro LeeAbstract:For landslide susceptibility mapping, this study applied and verified a Bayesian probability model, a likelihood ratio and statistical model, and logistic regression to Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS Satellite imagery and field surveys; and a spatial database was constructed from topographic maps, soil type, forest cover, geology and land cover. The factors that influence landslide occurrence, such as slope gradient, slope aspect, and curvature of topography, were calculated from the topographic database. Soil texture, material, drainage, and effective depth were extracted from the soil database, while forest type, diameter, and density were extracted from the forest database. Land cover was classified from Landsat TM Satellite imagery using unsupervised classification. The likelihood ratio and logistic regression coefficient were overlaid to determine each factor’s rating for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared with known landslide locations. The logistic regression model had higher prediction accuracy than the likelihood ratio model. The method can be used to reduce hazards associated with landslides and to land cover planning.
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Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea
International Journal of Remote Sensing, 2004Co-Authors: Saro Lee, Jaewon Choi, Kyungduck MinAbstract:The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) Satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) Satellite image. Landslide hazard areas were anal...
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Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea
Geomorphology, 2002Co-Authors: Saro Lee, Ueechan Chwae, Kyungduck MinAbstract:The purpose of this study is to develop and apply the technique for landslide susceptibility analysis using geological structure in a Geographic Information System (GIS). In the study area, the Janghung area of Korea, landslide locations were detected from Indian Remote Sensing (IRS) Satellite images by change detection, where the geological structure of foliation was surveyed and analysed. The landslide occurrence factors (location of landslide, geological structure and topography) were constructed into a spatial database. Then, strike and dip of the foliation and the aspect and slope of the topography were compared and the results, which were verified using landslide location data, show that foliation of gneiss has a geometrical relation to the joint or fault that leads to a landslide. Using the geometrical relations, the landslide susceptibility was assessed and verified. The verification results showed satisfactory agreement between the susceptibility map and the landslide location data.
