UNSUPERVISED SAR CHANGE DETECTION METHOD BASED ON REFINED SAMPLE SELECTION
In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hi...
| Published in: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-05-01
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| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/665/2022/isprs-annals-V-3-2022-665-2022.pdf |