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

Abstract

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 paying attention to spectral and spatial features. The raw HSI data are first preprocessed using a principal component analysis (PCA) operation because of the redundancy and correlation between HSI bands. Then, the entropy base informative module 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 3-D convolutional neural networks (CNNs) and 2-D CNNs with skip connections is exploited to reduce the complexity of the network compared to 3-D CNNs. The spatial attention module called depthwise spatial attention block can inherently highlight spatial information. The spectral attention module named reshape softmax attention can capture useful spectral regions of feature maps. Meticulous HSI classification tests are conducted over the University of Pavia, Indian Pines, Salinas, and Houston 2013 to evaluate the effectiveness of our approach. Our experiments show higher accuracy compared to other deep learning methods.

Keywords

• Deep learning
• depth-wise convolutional neural network (CNN)
• entropy filter
• hyperspectral image (HSI) classification
• remote sensing
• reshape softmax attention (RSA)