  In this paper, we propose a convolutional neural network with mapping layers
(MCNN) for hyperspectral image (HSI) classification. The proposed mapping
layers map the input patch into a low dimensional subspace by multilinear
algebra. We use our mapping layers to reduce the spectral and spatial
redundancy and maintain most energy of the input. The feature extracted by our
mapping layers can also reduce the number of following convolutional layers for
feature extraction. Our MCNN architecture avoids the declining accuracy with
increasing layers phenomenon of deep learning models for HSI classification and
also saves the training time for its effective mapping layers. Furthermore, we
impose the 3-D convolutional kernel on convolutional layer to extract the
spectral-spatial features for HSI. We tested our MCNN on three datasets of
Indian Pines, University of Pavia and Salinas, and we achieved the
classification accuracy of 98.3%, 99.5% and 99.3%, respectively. Experimental
results demonstrate that the proposed MCNN can significantly improve the
classification accuracy and save much time consumption.
