Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images

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.

Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

“…According to the relations among model parameters, p(Ψ) is further written as: p(Ψ) = p(α)p(m)p(w|m)p(µ m)p(σ|m). (7) means; and, σ 2 = {σlj 2 ; l = 1, ..., k, j = 1, ..., ml} is the set of variances.…”

“…Accurate segmentation of high-resolution remote sensing images plays an important role in obtaining detailed information of ground objects. Recently, various image segmentation algorithms have been proposed [4][5][6][7]. Among them, the statistical model-based segmentation algorithm has received extensive attention.…”

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

“…A paper by Lei et al [98] presents an unsupervised fuzzy clustering approach for landslide detection. The first step uses fuzzy c-means to generate landslide candidates.…”

End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping

“…Most of them depend on change detection that aims to detect the changed information of target at areas by analyzing the multi-temporal images acquired in different time of the same geographical area [2]. The popular ones can be roughly divided into three categories: threshold-based approaches [3,4], approaches based on feature extraction and feature classification [5][6][7], and deep learning approaches [8][9][10].…”

“…However, the performance of ELSE, RLSE, and CDMRF seriously depend on the quality of difference images and parameter selection. To reduce much dependencies, Lei et al [7] employed morphological reconstruction and a fast clustering approach to distinguish changed and unchanged areas for LM. The method provides better LM results than ELSE, RLSE, and CDFCM.…”