Automatic change detection in remote sensing images using level set method with neighborhood constraints

2014 ◽  
Vol 8 (1) ◽  
pp. 083678 ◽  
Author(s):  
Guo Cao ◽  
Yazhou Liu ◽  
Yanfeng Shang
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Level Set Method ◽  
Level Set ◽  
Remote Sensing Images

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

2018 ◽  
Vol 10 (9) ◽  
pp. 1381 ◽  
Author(s):  
Tao Lei ◽  
Dinghua Xue ◽  
Zhiyong Lv ◽  
Shuying Li ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Change Detection ◽  
Level Set ◽  
Execution Time ◽  
Remote Sensing Images ◽  
Fuzzy C Means ◽  
Landslide Mapping ◽  
Very High

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.

Region-driven distance regularized level set evolution for change detection in remote sensing images

2017 ◽  
Vol 76 (23) ◽  
pp. 24707-24722 ◽  
Author(s):  
Yu Lei ◽  
Jiao Shi ◽  
Jiaji Wu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Level Set ◽  
Remote Sensing Images ◽  
Level Set Evolution

scholarly journals Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

2021 ◽  
pp. 1-5
Author(s):  
Yi Liu ◽  
Chao Pang ◽  
Zongqian Zhan ◽  
Xiaomeng Zhang ◽  
Xue Yang
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Network Model ◽  
Dual Task ◽  
Remote Sensing Images ◽  
Convolutional Network

scholarly journals Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images

2021 ◽  
pp. 1-1
Author(s):  
Haeyun Lee ◽  
Kyung su Lee ◽  
Junhee Kim ◽  
Younghwan Na ◽  
Juhum Park ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Urban Land ◽  
Local Similarity ◽  
Land Change ◽  
Remote Sensing Images ◽  
Siamese Network
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