In this article, 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 redundancies 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 the convolutional layer to extract the spectral-spatial features for HSI. We tested our MCNN on three data sets 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 classification accuracy and save much time consumption.

In the re-evaluated paper, Xie et al. proposed a new fast search algorithm for vector quantization encoding, which optimized the priority checking order of variance and norm inequality in order to speed up the encoding procedure. CPU time of different encoding algorithms is given to support their algorithm. However, first, some of the experimental data in the re-evaluated paper are unreasonable and unrepeatable. And second, as an improved algorithm of equal-average equal-variance equal-norm nearest neighbor fast search algorithm, the re-evaluated algorithm in fact cannot achieve a better performance than the existing improved equal-average equal-variance nearest neighbor fast search algorithm. In this paper, these two problems are analyzed, re-evaluated, and discussed in detail.