Wavelet Attention ResNeXt Network for High-resolution Remote Sensing Scene Classification

Abstract

Deep learning algorithms have been used on a large scale in high-resolution remote sensing scene classification. However, traditional deep learning models usually suffer from incomplete consideration of spatial features, inadequate extraction of detail and texture features and difficulty in decoding deep features. In order to improve the extraction and generalization ability of convolutional neural networks for detail and texture features, a wavelet attention ResNeXt (WAResNeXt) is designed in this paper. The proposed WAResNeXt firstly extracts the multi-scale detail and texture information of the input feature map by wavelet transform, and then enhances the useful information and suppresses the redundant information by the attention mechanism. Finally, it reconstructs the feature map by the inverse wavelet transform. Experiments on the NWPU-RESISC45 dataset show that the WAResNeXt can effectively extract the spatial features and the texture features of high-resolution remote sensing images, and can greatly improve the scene classification accuracy.