Convolutional neural network based classification for hyperspectral data
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...[28] employed CNN to extract spectral features and achieved superior performance to SVM....
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...The basic idea of deep learning focuses more on how to increase the depth of the network architecture and reduce fitting parameters in each layer to extract feature information in high-dimensional space to achieve a higher classification accuracy and better performance[7]....
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...The nodes in next layer involve not only the strength of the responses, but also their spatial positions[9]....
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...These pooling layers can also be considered as “convolutional”, in the sense that they are using sliding windows[9]....
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...For classification purposes, 13 classes representing the various land cover types are defined for the set[10]....
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...As a result the hyperspectral remote sensing technology has become the leading technologies in many areas, and now it has an increasingly wide range of applications in both civil and military fields[3]....
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