Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey
Licheng Jiao, Zhongjian Huang, Xu Liu, Yuting Yang, Mengru Ma, Jiaxuan Zhao, Chao You, Biao Hou, Shuyuan Yang, Fang Liu, Wenping Ma, Lingling Li, Puhua Chen, Zhixi Feng, Xu Tang, Yuwei Guo, Xiangrong Zhang, Dou Quan, Shuang Wang, Weibin Li, Jing Bai, Yangyang Li, Ronghua Shang, Jie Feng
Abstract
Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.