Deep learning for processing and analysis of remote sensing big data: a technical review

Abstract

In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deeplearning-based 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 analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

“… As noted in Section 4.3.1 , the lack of large benchmark datasets is a bottleneck in water body detection and water quality monitoring research utilizing RS imagery and AI. The dominant methods in both water domains are supervised learning, which often requires very large, labeled datasets to train on, thus, there is a clear, urgent need for semi-supervised and unsupervised learning methods [ 15 ]. Unsupervised learning methods are able to learn from big sets of unlabeled data, as demonstrated in [ 29 , 46 ].…”

“… Most current ML/DL-based RS research focuses on borrowing or slightly improving ML/DL/CV models from computer science [ 79 , 120 ]. Compared with natural scene images, RS data are multiresolution, multitemporal, multispectral, multiview, and multitarget [ 15 ]. Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data.…”

“…Slight modifications of ML/DL/CV models simply cannot cope with the special challenges posed in RS big data. New ML/DL models specialized for RS big data are thus urgently needed [ 15 , 18 ]. We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue.…”

“…We hope our review will draw the attention of researchers who have a multidisciplinary background to this issue. Looking deep into the mechanisms of RS and land surface processes, studying the characteristics of RS imagery would guide the design of specialized ML/DL models for RS big data and thus further improve RS applications using AI in breadth and depth [ 15 ].…”

“…Remote sensing (RS) is the single largest source of geospatial big data and has increased dramatically in terms of both spatial and temporal resolution. This poses serious challenges for effective and efficient processing and analysis [ 15 ]. Meanwhile, recent advances in DL and CV have significantly improved research in RS and geosciences [ 16 , 17 , 18 ].…”