DESSA-Net Model: Hyperspectral Image Classification Using an Entropy Filter With Spatial and Spectral Attention Modules on DeepNet

Recent advancements in remote sensing technology have significantly expanded the exploration of natural resources and enabled the detection of materials in inaccessible areas. Hyperspectral images (HSIs) are a valuable data source due to their distinctive properties in various applications. However, several problems, including noise, band correlation, ineffectively extracted features, and most notably, a lack of sufficient labeled samples reduce the accuracy of HSI classification. To improve the performance of such a system, we propose an effective method with the capability of making attention to spectral and spatial features. The raw HSI data is first preprocessed using a Principal Component Analysis (PCA) operation because of the redundancy and correlation between HSI bands. Then, the Entropy Base Informative Module (EBIM) is designed to add entropy information to the selected spectral features by PCA. We also use spectral and spatial attention modules in the proposed model. Moreover, a hybrid neural network that uses both 3D CNNs and 2D CNNs with skip connections is exploited to reduce the complexity of the network compared to 3D CNNs. The spatial attention module called Depth-wise Spatial Attetnion (DSA) block can inherently highlight spatial information. The spectral attention module named Reshape Softmax Attention (RSA) can capture useful spectral regions of feature maps. Meticulous HSI classification tests are conducted over the University of Pavia (PU), Indian Pines (IP), Salinas (SA), and Houston 2013 (HT) to evaluate the effectiveness of our approach. Our experiments show higher accuracy compared to other deep learning methods.