Unsupervised Change Detection in VHR Images Based on Morphological Profiles and Automated Training Sample Extraction

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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