Sparse representation with spike convolutional neural networks for scene classification of remote sensing images of high resolution

Remote sensing imagery scene classification is of great significance to land resource management. However, the distribution of ground objects in remote sensing images of high spatial resolution (HSR) is complex, and there is redundant information irrelevant to the current scene in the images, which will affect the accurate classification of the scene. To solve this problem, a scene classification method based on the spike convolutional neural network (SCNN) is proposed. From the perspective of sparse representation, the SCNN is designed based on the sparse spike output characteristics of spike neurons to remove the redundant information irrelevant to the scene in remote sensing images and realize sparse representation of images. A backpropagation algorithm based on the spike output cross entropy loss function is proposed. Based on this algorithm, the SCNN is trained by gradient descent, and the network parameters are optimized to realize scene classification of remote sensing images. The validity of the proposed method is verified by experiments, where the proposed method is applied to two remote sensing imagery datasets, namely, Google and UCM, and compared with the traditional convolutional neural network (CNN). Experimental results show that the proposed method is able to perform sparse representation of remote sensing images and realize scene classification; and compared with CNN, the proposed method shows better performance in the remote sensing imagery scene classification task. © 2022 Northeast University. All rights reserved.