Differential Self-Feedback Dilated Convolution Network With Dual-Tree Channel Attention Mechanism for Hyperspectral Image Classification

Overfitting and inadequate ability of feature extraction result in low classification accuracy when fulfilling hyperspectral image (HSI) classification tasks by deep learning models (DLMs). Unfortunately, it is very common that insufficient labeled training samples of HSI lead to overfitting and that redundant spectral information of HSI asks for high feature extraction ability in classification tasks. In general, reducing model complexity helps alleviate overfitting, whereas increasing the number of convolutional layers and kernel size helps enhance feature extraction ability. Designing DLMs for better classification accuracy requires balancing model complexity and feature extraction ability. To achieve this balance, we present a network model of differential self-feedback dilated convolution with dual-tree channel attention mechanism (DSDC-DCAM) model. In this model, a differential self-feedback dilated convolution (DSDC) block is proposed with high extraction ability and low model complexity by connecting 3-D dilated convolutions and the differential self-feedback (DSF) module as a tree structure. A dual-tree channel attention mechanism (DCAM) block is also proposed in DSDC-DCAM to form a better attention mechanism in order to exploit useful features and suppress unfavorable ones while reducing model complexity. Experimental results show that DSDC-DCAM has the best performance consistency compared with the other nine state-of-the-art models, indicating that it is least influenced by overfitting. Meanwhile, the results show that DSDC-DCAM achieves the highest overall accuracy (OA) with a smaller number of model parameters than the other seven models.