Abstract
In the present work the searching capability of Genetic Algorithms (GAs) is exploited to evolve suitable Hopfield type neural network architectures for optimum change detection of multitemporal remotely sensed images. Experiments carried out on two remote sensing images confirm the effectiveness of the proposed technique.
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Mandal, A., Ghosh, S., Ghosh, A. (2011). Neuro-Genetic Approach for Detecting Changes in Multitemporal Remotely Sensed Images. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_52
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DOI: https://doi.org/10.1007/978-3-642-21786-9_52
Publisher Name: Springer, Berlin, Heidelberg
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