H2AN: Hierarchical Homogeneity-Attention Network for Hyperspectral Image Classification

Recently, a self-attention network (SAN) is developed as an effective strategy to extract features from attention areas for image classification. However, for hyperspectral image (HSI), the lack of position supervision of object regions and inefficient similarity computation lead to unsatisfactory classification performance on mixed pixels. To alleviate the above two problems for HSI image classification, we propose a novel hierarchical homogeneity-attention network (H2AN) in this article. First, we design a homogeneity-attention block (HAB) to depict the feature correlation with the homogeneous mask. Using the supervision of homogeneity mask, we can calculate the attention guided by the predefined number of homogeneity embeddings, which can reduce the heavy computation instead of the global search in self-attention block (SAB) of SAN. Second, we propose a hierarchical convolutional neural network (HCNN) inserting the HAB into different levels of network cells for highly efficient feature extraction of target regions, named H2AN. Because of the transferring of homogeneity property from shallow layer to deep layer, our H2AN outperforms the state-of-the-art methods in qualitative and quantitative experiments on three typical datasets.