Unsupervised Change Detection in Multi-temporal Satellite Images Based on Structural Patch Decomposition and k-means Clustering for Landslide Monitoring
| Autor: | Asadang Tanatipuknon, Pakinee Aimmanee, Jessada Karnjana, Suthum Keerativittayanun |
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| Rok vydání: | 2020 |
| Předmět: |
Pixel
business.industry Computer science Binary image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION k-means clustering Word error rate Pattern recognition Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Principal component analysis Artificial intelligence Cluster analysis business Change detection |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030625085 IUKM |
| Popis: | This paper proposes a new, simple, and effective unsupervised change detection method for multi-temporal satellite imagery in the landslide monitoring system. The method combines a modified version of k-means clustering, so called the adaptive k-means clustering algorithm and a structural consistency map to binarize input multi-temporal satellite images. The absolute differences between pairs of contiguous binary images are used to construct a change map. The structural consistency map is generated by a method based on the structural patch decomposition. The evaluation results show that the proposed method is considerably better than a conventional method based on principal component analysis (PCA) and k-means clustering in terms of pixel error rate, correctness, and robustness against noise addition. For the pixel error rate and the correctness, the improvements are approximately \(42.61\%\) and \(13.92\%\), respectively. Also, when noises are added into the input images, the pixel error rate of the proposed method is improved by \(98\%\) from that of the PCA-based method, and the correctness is improved by \(77.27\%\). |
| Databáze: | OpenAIRE |
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