Abstract
An effective method based on evidence theory and neural networks to classify the remote sensing images is brought forward in the paper. Firstly, with the spatial information in consideration, the original image is smoothed with a modified gradient inverse weighting smoothing method, then the classification of the original and smoothed images is performed separately using a B-P neural network. Finally, result comes out after fusing the two classification results with D-S evidence theory. Experimental results demonstrate that the proposed method is effective and can improve the classification accuracy.
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© 2004 Springer-Verlag Berlin Heidelberg
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Chen, G., Li, BC., Guo, ZG. (2004). Remote Sensing Image Classification Based on Evidence Theory and Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_160
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DOI: https://doi.org/10.1007/978-3-540-28647-9_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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