Classification of hyperspectral imagery with a 3D convolutional neural network and J-M distance

Abstract A three-dimensional convolutional neural network (3D-CNN) is proposed and applied to the identification of land types from hyperspectral images. Due to the advantages of the combined use of the spectral - spatial features of hyperspectral imagery, high accuracy identification of most objects can be realized. However, too many wavelengths increase information redundancy between adjacent bands and interfere with classification accuracy to some extent, complicating achievement of better results for the identification of ground objects with relatively small differences in spectral and spatial domains. To solve such problems, in this paper, the Jeffries-Matusita (J-M) distance is introduced to select effective bands to reduce the redundancy of spectral information for identifying objects with similar features. This method is based on a 3D-CNN considering pixel spectral and spatial information. The optimal band combination algorithm based on the J-M distance is first used to extract spectral features of hyperspectral data while reducing feature dimensions. Then, the 3D-CNN is applied to mine spectral and spatial features from the hyperspectral images. Finally, a Softmax classifier is used to classify land types based on the high-level features learned by the 3D-CNN. Experiments were carried out on data from an area of north-western Indiana and a Pavia university scene. In such images, some objects have very similar spectral and spatial features. The results were compared with the current 3D-CNN land type classification and show that both methods can achieve high-precision identification of most land types, but for objects with similar features, the method proposed in this article has obvious advantages.