Context Residual Attention Network for Remote Sensing Scene Classification

Remote sensing image scene classification has attracted much attention due to its wide application. In this paper, a new end-to-end attentive context network under the guidance of human visual system has been proposed. The network can focus on some critical regions of the image selectively and then extract high-level feature information so as to generalize the whole image. The contributions of this letter are as follows: firstly, a novel attention structure which expresses the attention of image by stacking attention modules is designed; secondly, the context information of the image is utilized to make a holistic analysis of the attention mechanism on the basis of the top-down feedforward structure. Experiments performed on several datasets demonstrate that the proposed framework can obtain outstanding performance compared with state-of-the-art approaches.