Spectral-spatial inverted residual attention network for hyperspectral image classification with pre-activation

Abstract

In recent years, hyperspectral image (HSI) classification technology has received more attention. Deep learning methods have been gradually applied to the classification of HSIs. Convolutional neural networks-based model, especially the residual networks (ResNets), have shown its excellent performance. In HSI samples, there are usually some noise pixels near the center pixel, which are not conductive for the extraction of spectral-spatial features and will have a negative impact on the classification performance. In our previous study, a spectral similarity-based spatial attention module integrated with 3D Resnet was designed to highlight the effect of center pixels on spatial attention. However, during the generation of the spatial attention, the characteristics of different similarities are ignored. Meanwhile, the employment of 3D ResNet may generate a large amount of redundancy to waste the computing resources. Therefore, an improved spectral-similarity-based spatial attention module with pre-activation and a 3D inverted residual attention network, is proposed. The pre-activation strategy is designed to follow the feature of each similarity measurement, in which the Canberra distance is invoked to simplify the computational complexity of similarity. A 3D inverted residual module is integrated with squeeze-and-excite attention modules to handle spatial and spectral-spatial features more efficiently. Experimental results on three public HSI data sets demonstrate a better classification performance of the proposal comparing with some of the state of the arts.