Abstract
A new subpixel mapping method based on BP neural network is proposed to improve spatial resolution of both raw hyperspectral imagery (HSI) and its fractional image. The network is used to train a model that describes the relationship between mixed pixel accompanied by its neighbors and the spatial distribution within the pixel. Then mixed pixel can be super-resolved by the trained model in subpixel scale. To improve the mapping performance, momentum is employed in BP learning algorithm and local analysis is adopted in processing of raw HSI. The comparison experiments are conducted both on synthetic images and on truth HSI. The results prove that the method has fairly good mapping effect and very low computational complexity for processing both of raw HSI and of fractional image.
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Wang, L., Zhang, Y., Li, J. (2006). BP Neural Network Based SubPixel Mapping Method. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_87
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DOI: https://doi.org/10.1007/978-3-540-37258-5_87
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