Abstract
Change detection approaches based on image segmentation are often used for landslide mapping (LM) from very high-resolution (VHR) remote sensing images. However, these approaches usually have two limitations. One is that they are sensitive to thresholds used for image segmentation and require too many parameters. The other one is that the computational complexity of these approaches depends on the image size, and thus they require a long execution time for very high-resolution (VHR) remote sensing images. In this paper, an unsupervised change detection using fast fuzzy c-means clustering (CDFFCM) for LM is proposed. The proposed CDFFCM has two contributions. The first is that we employ a Gaussian pyramid-based fast fuzzy c-means (FCM) clustering algorithm to obtain candidate landslide regions that have a better visual effect due to the utilization of image spatial information. The second is that we use the difference of image structure information instead of grayscale difference to obtain more accurate landslide regions. Three comparative approaches, edge-based level-set (ELSE), region-based level-set (RLSE), and change detection-based Markov random field (CDMRF), and the proposed CDFFCM are evaluated in three true landslide cases in the Lantau area of Hong Kong. The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.
Highlights
Change detection is used to detect the changed information of the target area by analyzing the multi-temporal images acquired in different periods of the same geographical area
To address the issues mentioned above, we propose a change detection approach based on fast fuzzy c-means (FCM) clustering (CDFFCM) and apply it to landslide mapping (LM)
A change detection approach using fast fuzzy c-means clustering for landslide mapping (CDFFCM) has been presented in this paper
Summary
Change detection is used to detect the changed information of the target area by analyzing the multi-temporal images acquired in different periods of the same geographical area. Though the approaches based on the combination of feature transform and clustering [14,15,16] provide better results than threshold segmentation-based approaches for change detection, these approaches are only suitable for images that have a clear difference between high frequency and low frequency, but unsuitable for many remote sensing images because of the complex background, texture, and illumination of images [17]. They usually fail to detect changed regions for images that have a complex background and blurred edges. The post-processing is fast because it is performed on binary images
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