Deep learning for change detection in remote sensing: a review
Wuhan Science and Technology Bureau · Wuhan Academy of Agricultural Sciences · +6 more institutions
Abstract
ABSTRACTA large number of publications have incorporated deep learning in the process of remote sensing change detection. In these Deep Learning Change Detection (DLCD) publications, deep learning methods have demonstrated their superiority over conventional change detection methods. However, the theoretical underpinnings of why deep learning improves the performance of change detection remain unresolved. As of today, few in-depth reviews have investigated the mechanisms of DLCD. Without such a review, five critical questions remain unclear. Does DLCD provide improved information representation for change detection? If so, how? How to select an appropriate DLCD method and why? How much does each type of change…
Citation impact
- FWCI
- 28.47
- Percentile
- 100%
- References
- 138
Authors
7- TBTing Bai
Wuhan Science and Technology Bureau, Wuhan Academy of Agricultural Sciences
- LWLe WangCorresponding
University at Buffalo, State University of New York
- DYDameng Yin
Chinese Academy of Agricultural Sciences, Institute of Crop Sciences
- KSKaimin Sun
Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
- YCYepei Chen
Hubei University of Technology
Topics & keywords
- Change detection
- Remote sensing
- Deep learning
- Computer science
- Environmental science
- Artificial intelligence
- Data science
- Geology
- Industry, innovation and infrastructure