Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network

Authors:
Swarnajyoti Patra;Susmita Ghosh;Ashish Ghosh
Affiliations:
Jadavpur University, India;Jadavpur University, India;Indian Statistical Institute, India
Venue:
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Year:
2007

Citing 0
Cited 4

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

International Journal of Approximate Reasoning
Semi-supervised learning with multilayer perceptron for detecting changes of remote sensing images

PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron

Fundamenta Informaticae
Time-series processing of large scale remote sensing data with extreme learning machine

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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 dimension of the input patterns. The network is updated depending on some threshold value and when the network converges status of output neurons depict the change-detection map. To select a suitable threshold for initialization of the network, a correlation based and an energy based criteria are suggested. Experimental results, carried out on two multispectral remote sensing images, confirm the effectiveness of the proposed approach.