Spatial Attention Guided Residual Attention Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has become a research hotspot. Recently, deep learning-based methods have achieved preferable performances by which the deep spectral-spatial features can be extracted from HSI cubes. However, in complex scenes, due to the diversity of the types of land-cover and the bands in high dimensional, these methods are often hampered by the irrelevant spatial areas and the redundant bands, which results in the indistinguishable features and the restricted performance. In this article, a spatial attention guided residual attention network (SpaAG-RAN) is proposed for HSI classification, which contains a spatial attention module (SpaAM), a spectral attention module (SpeAM), and a spectral-spatial feature extraction module (SSFEM). Based on the spectral similarity, the SpaAM is capable of capturing the relevant spatial areas composed of the pixels of the same category as the center pixel from HSI cube with a novel inverted-shifted-scaled sigmoid activation function. The SpeAM aims to select the bands which are beneficial to the spectral features representation. The SSFEM is exploited to extract the discriminating spectral-spatial features. To facilitate the processes of bands selection and features extraction, two well-designed spatial attention masks generated by the SpaAM are employed to guide the works of the SpeAM and the SSFEM, respectively. Moreover, a spatial consistency loss function is installed to maintain the consistency between the two spatial attention masks so that the network enables the distinction of the relevant features exactly. Experimental results on three HSI data sets show that the proposed SpaAG-RAN model can extract the discriminating spectral-spatial features and outperforms the state-of-the-arts.

Automated summary

This article introduces a SpaAG-RAN model for HSI classification, which extracts discriminating spectral-spatial features through spatial attention guided residual attention network and achieves better performance than existing methods.