Progressive multi-scale multi-attention fusion for hyperspectral image classification

In recent years, due to the unique spatial-spectral characteristics of hyperspectral images, they have played a crucial role in many fields. The effective extraction of features using deep neural networks, followed by the design of efficient and high-precision network algorithm structures, has gradually become a research hotspot. Hyperspectral images are difficult to obtain and have limited samples. Although hyperspectral image classification methods based on convolutional neural networks (CNN) have noticeably improved performance, there are still certain shortcomings in the extraction of detailed and local features. Therefore, how to fully utilize spatial and spectral information in situations with limited samples has become a challenging problem. To address this issue, inspired by the PID controller, this paper proposes a Progressive Multi-Scale Multi-Attention Fusion (PMMF) network structure that simultaneously extracts features from the Proportional (P), Integral (I), and Derivative (D) branches. The complementary responsibilities of the three branches address the issue of feature loss in details and improve the network's learning efficiency across feature maps of different scales. By cleverly extracting features from different branches multiple times, the fusion of multi-scale features is achieved, avoiding the limitations of single-scale feature representation. The proposed multi-attention fusion module applies the most suitable attention mechanism according to the representation form of each branch, fully extracting features from each branch, enriching the information contained in the feature maps, and greatly enhancing the classification accuracy of hyperspectral images.