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

被引：45

作者：

Zhang, Xin ^{[1
]}

Zhou, Ya'nan ^{[2
]}

Luo, Jiancheng ^{[1
]}

机构：

[1] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[2] Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Peoples R China

来源：

BIG EARTH DATA | 2022年 / 6卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Remote sensing; big data; deep learning; technical review; CONVOLUTIONAL NEURAL-NETWORK; POINT CLOUD CLASSIFICATION; WATER BODY EXTRACTION; PAN-SHARPENING METHOD; FASTER R-CNN; LAND-COVER; OBJECT DETECTION; TIME-SERIES; BUILDING EXTRACTION; DETECTION ALGORITHM;

D O I：

10.1080/20964471.2021.1964879

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 deep-learning-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, land-use/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.

引用

页码：527 / 560

页数：34

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