Multiscale 3-D-CNN based on spatial–spectral joint feature extraction for hyperspectral remote sensing images classification

Abstract

In order to make full use of the spatial–spectral information of hyperspectral images (HSIs), a multiscale three-dimensional (3-D) convolutional neural network (CNN) model is proposed to extract the features of HSIs and classify them. First, the principal component analysis method is used to reduce the dimension of the original HSI, reduce the spectrum size on the basis of retaining the majority of spectrum information, and improve the training speed of the model. Second, at present, most of the HSI classification methods are based on spectral features and ignore the pixels’ neighborhood information on each band, thus a 3-D-CNN model is proposed for convolution feature extraction using a spatial–spectral joint method, which makes full use of the spatial–spectral information of HSI and greatly improves the classification effect of the model. Finally, traditional CNN models mostly use single-size convolution kernels. Several convolution kernels of different sizes are added to the convolution layer to extract the spatial features of different sizes, which effectively improves the classification ability of the model. The experimental results show that the multiscale 3-D-CNN model designed, when used on three datasets: Indian Pines, Pavia University, and Salinas, reached 97.54%, 99.78%, and 99.24% overall classification accuracy, respectively. Compared with the traditional single-scale two-dimensional-CNN model, the overall classification accuracy is improved by about 1% to 2%, which can better complete the classification task of HSIs.