Two unsupervised context-sensitive change detection techniques, one based on Hopfield type neural network and the other based on self-organizing feature map neural network, for remote sensing images have been proposed in this chapter. In the presented Hopfield network, each neuron corresponds to a pixel in the difference image and is assumed to be connected to all its neighbors. An energy function is defined to represent the overall status of the network. Each neuron is assigned a status value depending on an initialization threshold and updated iteratively until converges. On the other hand, in the self-organizing feature map model, number of neurons in the output layer is equal to the number of pixels in the difference image 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. For both the cases, at convergence, the output statuses of neurons represent a change detection map. Experimental results confirm the effectiveness of the proposed approaches.
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Ghosh, S., Patra, S., Ghosh, A. (2008). A Neural Approach to Unsupervised Change Detection of Remote-Sensing Images. In: Prasad, B., Prasanna, S.R.M. (eds) Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. Studies in Computational Intelligence, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75398-8_11
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