Context Residual Attention Network for Remote Sensing Scene Classification
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS(2022)
摘要
Remote sensing image scene classification has attracted much attention due to its wide application. In this letter, 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: 1) a novel attention structure which expresses the attention of image by stacking attention modules is designed and 2) the context information of the image is used 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 the state-of-the-art approaches.
更多查看译文
关键词
Remote sensing,Sensors,Deep learning,Training,Satellites,Image resolution,Task analysis,Context residual attention network (CRAN),remote sensing,scene classification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要