DA2Net: Distraction-Attention-Driven Adversarial Network for Robust Remote Sensing Image Scene Classification

Optical remote sensing image (RSI) is easily affected by weather conditions. When the ground target is sheltered by clouds, extracting scene information from the RSI becomes quite challenging. In this work, we propose a distraction-attention-driven adversarial training network (DA2Net) to learn a robust RSI scene classification model. The distraction module employs a gradient-based class activation mapping (GradCAM++) method to produce partially occluded samples. Through feature map visualization, GradCAM++ can quantify the contribution of each region to the network prediction. Regions in the input image are erased and filled with white pixels if the corresponding contribution is higher than a given threshold. In this way, the distraction module enriches the training sample diversity and benefits the network's robustness and generalization performance. Training with the partially erased samples, the model can extract sufficient information from other regions even though the target with prominent features is occluded. The attention module highlights important features and information. It encourages the network to mine critical features from the uncovered regions. Competition between the two modules drives the network to improve its robustness and overall performance. Extensive experiments show that the DA2Net provides a promising approach for data augmentation and network training. Analysis of cloud-covered scene classification demonstrates the DA2Net's robust performance.