In recent years,the rapid development of Earth observation tech-nology has produced an increasing growth in remote sensing big data,posing serious challenges for effective and efficient proces-sing and analysis.Meanwh...In recent years,the rapid development of Earth observation tech-nology has produced an increasing growth in remote sensing big data,posing serious challenges for effective and efficient proces-sing and analysis.Meanwhile,there has been a massive rise in deeplearningbased algorithms for remote sensing tasks,providing a large opportunity for remote sensing big data.In this article,we initially summarize the features of remote sensing big data.Subsequently,following the pipeline of remote sensing tasks,a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data,including geometric and radiometric processing,cloud masking,data fusion,object detection and extraction,landuse/cover classification,change detection and multitemporal ana-lysis.Finally,we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.展开更多
基金supported in part by the National Key Research and Development Program under Grant[2017YFB0504201]the National Natural Science Foundation of China under Grant Nos.[42071316,61473286 and 401201460]+1 种基金Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.[OFSLRSS201919]the Fundamental Research Funds for the Central Universities under Grant No.[B200202008].
文摘In recent years,the rapid development of Earth observation tech-nology has produced an increasing growth in remote sensing big data,posing serious challenges for effective and efficient proces-sing and analysis.Meanwhile,there has been a massive rise in deeplearningbased algorithms for remote sensing tasks,providing a large opportunity for remote sensing big data.In this article,we initially summarize the features of remote sensing big data.Subsequently,following the pipeline of remote sensing tasks,a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data,including geometric and radiometric processing,cloud masking,data fusion,object detection and extraction,landuse/cover classification,change detection and multitemporal ana-lysis.Finally,we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.
