Physics-informed interactive network for hyperspectral image classification

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have made great progress. In recent CNN-based HSI classification methods, spatial attention mechanism has been widely used to emphasize more salient features and suppress less useful ones. However, these methods tend to utilize plug-and-play attention modules which ignore the physical meaning of HSI pixels. To reflect the physical construction of HSI pixels in the attention mask, we propose a physics-informed interactive network (PI²Net) integrating a spatial guided spectral component stream (SGSC-stream) and a spectral constrained spatial attention stream (SCSA-stream). The SGSC-stream can obtain more expressive abundance features of the input HSI cubes through a component analysis block. The SCSA-stream completes the prediction of hyperspectral data in three-stage features extraction cells and a classification cell. To connect and interact between these two streams, a component-aware attention module (CAAM) and a class-wise endmember activation gate (CWEAG) are designed. The CAAM measures the similarity of abundance features to generate a component-aware attention mask, so as to learn the component-based relationships between pixels. Then, the multiscale attention masks are embedded into different stages of the SCSA-stream. The CWEAG is proposed to iteratively generate more accurate abundance features with the classification result of SCSA-stream. Experimental results based on three common hyperspectral data sets demonstrate that PI²Net outperforms several state-of-the-art methods in terms of qualitative and quantitative analysis.