Unsupervised Change Detection in VHR Images Based on Morphological Profiles and Automated Training Sample Extraction
2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)(2019)
Abstract
VHR remote sensing image change detection with pixel-based method often results in some problems that have negative effects on accuracy, such as the salt-and-pepper noise. In order to achieve a better result under this circumstance, an unsupervised sequential strategy combining Morphological Profiles and automated training sample extraction is introduced. Change detection with two real multi-temporal VHR datasets were carried out to test the effectiveness of the proposed approach. The experimental results showed that this approach outperformed the traditional unsupervised change detection methods in terms of accuracy and visual effect.
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Key words
Morphological Profiles (MPs),Change Vector Analysis (CVA),Markov Random Field (MRF),automated training sample extraction (ATSE)
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