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UNSUPERVISED SAR CHANGE DETECTION METHOD BASED ON REFINED SAMPLE SELECTION.
- Source :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2022, Issue 3, p665-672, 8p
- Publication Year :
- 2022
-
Abstract
- 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-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21949042
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 158183930
- Full Text :
- https://doi.org/10.5194/isprs-annals-V-3-2022-665-2022