Modified self-organizing feature map neural network with semi-supervision for change detection in remotely sensed images

Authors:
Susmita Ghosh;Moumita Roy
Affiliations:
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

Citing 6
Cited 1

Neural network, self-organization and object extraction

Pattern Recognition Letters - Special issue on artificial neural networks
Self-organizing maps

Self-organizing maps
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron

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

International Journal of Approximate Reasoning
Semi-Supervised Learning

Semi-Supervised Learning
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Information Sciences: an International Journal

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

Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semisupervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In this method, training of theMSOFMis performed iteratively using unlabeled patterns along with a few labeled patterns. A method has been suggested to select unlabeled patterns for training. To check the effectiveness of the proposed methodology, experiments are carried out on two multitemporal remotely sensed images. Results are found to be encouraging.