Hyperspectral image classification via local receptive fields based random weights networks

This paper proposes a classification approach for hyperspectral image using the local receptive fields based random weights networks. The local receptive field concept originates from research in neuroscience. Considering the local correlations of spectral features, it is promising to improve the performance of HSI classification by introducing the local receptive fields. The proposed classification framework consists of four layers, i.e., input layer, convolution layer, pooling layer, and output layer. The convolution and pooling layer are used for feature extracting and the last layer is used as the classifier. Experimental results on the ROSIS Pavia University dataset confirm the effectiveness of the proposed HSI classification method.