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机构地区:[1]Dept.of Information Engineering,Harbin Institute of Technology [2]College of Information and Communications Engineering,Harbin Engineering University
出 处:《Journal of Harbin Institute of Technology(New Series)》2009年第2期279-283,共5页哈尔滨工业大学学报(英文版)
基 金:Sponsored by the National Natural Science Foundation of China(Grant No. 60272073, 60402025 and 60802059);by Foundation for the Doctoral Program of Higher Education of China (Grant No. 200802171003)
摘 要:A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
关 键 词:sub-pixel mapping BP neural network BP learning algorithm with momentum
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