Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel Attentional Feature Fusion

Hyperspectral images (HSIs) have abundant spectral and spatial information, which shows bright prospects in the application industry of urban-rural. Thus, HSI classification has drawn much attention from researchers. However, the spectral and spatial information-extracting method is one of the research difficulties in HSI classification tasks. To meet this tough challenge, we propose an efficient channel attentional feature fusion dense network (CA-FFDN). Our network has two structures. In the feature extraction structure, we utilized a novel bottleneck based on separable convolution (SC-bottleneck) and efficient channel attention (ECA) to simultaneously fuse spatial-spectral features from different depths, which can make full use of the dual-scale shallow and deep spatial-spectral features of the HSI and also significantly reduce the parameters. In the feature enhancement structure, we used 3D convolution and average pooling to further integrate spatial-spectral features. Many experiments on Indian Pines (IP), University of Pavia (UP), and Kennedy Space Center (KSC) datasets demonstrated that our CA-FFDN outperformed the other five state-of-the-art networks, even with small training samples. Meanwhile, our CA-FFDN achieved classification accuracies of 99.51%, 99.91%, and 99.89%, respectively, in the case where the ratio of the IP, UP, and KSC datasets was 2:1:7, 1:1:8, and 2:1:7. It provided the best classification performance with the highest accuracy, fastest convergence, and slightest training and validation loss fluctuations.