An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network

Authors:
Susmita Ghosh;Swarnajyoti Patra;Ashish Ghosh
Affiliations:
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700 032, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata 700 032, India;Machine Intelligence Unit and Center for Soft Computing Research, Indian Statistical Institute, 203 B.T. Road, Kolkata 700 108, India
Venue:
International Journal of Approximate Reasoning
Year:
2009

Citing 6
Cited 6

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CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network

ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition

Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition
An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

IEEE Transactions on Image Processing
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IEEE Transactions on Neural Networks

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Information Sciences: an International Journal
Modified self-organizing feature map neural network with semi-supervision for change detection in remotely sensed images

PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images

Applied Soft Computing
Semi-supervised change detection using modified self-organizing feature map neural network

Applied Soft Computing
Change detection in remotely sensed images using semi-supervised clustering algorithms

International Journal of Knowledge Engineering and Soft Data Paradigms
A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system

Information Sciences: an International Journal

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Abstract

In this paper, we propose an unsupervised context-sensitive technique for change-detection in multitemporal remote sensing images. Here a modified self-organizing feature map neural network is used. Each spatial position of the input image corresponds to a neuron in the output layer and the number of neurons in the input layer is equal to the number of features of the input patterns. The network is updated depending on some threshold value and when the network converges, status of output neurons depict a change-detection map. To select a suitable threshold of the network, a correlation based and an energy based criteria are suggested. The proposed change-detection technique is unsupervised and distribution free. Experimental results, carried out on two multispectral and multitemporal remote sensing images, confirm the effectiveness of the proposed approach.