A Feature Complementary Attention Network Based on Adaptive Knowledge Filtering for Hyperspectral Image Classification

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification (HSIC). However, the size of the convolutional kernel in CNNs is fixed, which makes it difficult to capture the dependence of long-range feature information. In addition, the extracted features often contain a large amount of redundant information. In order to alleviate these issues, a feature complementary attention network based on adaptive knowledge filtering (FCAN_AKF) is proposed in this article. First, in order to alleviate the problem that CNNs are difficult to capture the dependence between close-range and long-range spectral features due to the limited receptive field, a nonlocal band regrouping (NBR) strategy is designed. NBR enables CNN to capture nonlocal spectral features in a limited receptive field to establish the interdependence between close-range and long-range spectral features. In addition, the nonlocal features extracted after using NBR and the local features of the original hyperspectral image are integrated to achieve complementation between nonlocal features and local features. Then, in order to eliminate the interference of redundant information on the network, a dual-pyramid spectral–spatial attention (DPSSA) module is proposed and used to capture spectral–spatial attention. Next, an adaptive knowledge filter (AKF) is designed, which can adaptively further filter out redundant information and enhance feature information that is beneficial for classification. Finally, extensive experiments were conducted on three challenging datasets, demonstrating that the proposed method has stronger competitiveness compared to some state-of-the-art HSIC methods.