S
Susmita Ghosh
Researcher at Jadavpur University
Publications - 175
Citations - 2540
Susmita Ghosh is an academic researcher from Jadavpur University. The author has contributed to research in topics: Change detection & Image segmentation. The author has an hindex of 22, co-authored 139 publications receiving 1958 citations. Previous affiliations of Susmita Ghosh include University of Calcutta & Noakhali Science and Technology University.
Papers
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Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
TL;DR: A context-sensitive technique for unsupervised change detection in multitemporal remote sensing images based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times.
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A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks
TL;DR: A context-sensitive technique for unsupervised change detection in multitemporal remote sensing images based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times is proposed.
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Self-adaptive differential evolution for feature selection in hyperspectral image data
TL;DR: An attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms for hyperspectral images and shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.
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Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images
TL;DR: A new technique for incorporation of local information for unsupervised change detection in multitemporal remote sensing images is introduced, which is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.
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Histogram thresholding for unsupervised change detection of remote sensing images
TL;DR: Among all the thresholding techniques investigated here, Liu's fuzzy entropy followed by Kapur's entropy are found to be the most robust techniques.