A Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 11; p. 1135
• Main Authors: Liu, Wensong, Yang, Jie, Zhao, Jinqi, Yang, Le
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2017
• Subjects: Accuracy; Algorithms; Change detection; False alarms; generalized Gaussian mixture model (GMM); generalized statistical region merging (GSRM); Image acquisition; Image detection; Image processing; Image segmentation; Methods; Noise; PolSAR; Radar imaging; Radar polarimetry; Radarsat; Remote sensing; Statistical analysis; Synthetic aperture radar; test statistic; unsupervised change detection
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9111135

The existing unsupervised change detection methods using full-polarimetric synthetic aperture radar (PolSAR) do not use all the polarimetric information, and the results are subject to the influence of noise. In order to solve these problems, a novel automatic and unsupervised change detection appro...