| Autor: |
HUANG Pingping, REN Huifang, TAN Weixian, DUAN Yinghong, XU Wei, LIU Fang |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Leida xuebao, Vol 9, Iss 3, Pp 514-524 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2095-283X |
| DOI: |
10.12000/JR20004?viewType=HTML |
| Popis: |
Ground-based radar is a microwave remote sensing imaging technology that has been gradually developed throughout the past 20 years so that it has become mature. At present, it has been widely used in monitoring geological disasters such as landslides and collapses. Ground-based radars can detect microvariations in target areas through the principle of interferometry. However, due to human factors, geological factors, and meteorological factors, the radar image of the monitored area is incoherent, which makes long-term quantitative monitoring difficult. Therefore, further developing the application of change detection while considering quantitative monitoring is urgent, to provide effective information on long-term changes and comprehensively understand the dynamic changes in the monitored area. To solve the above problems, an unsupervised change detection method using ground-based radar images and based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the Nonsubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, principal component analysis is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of the ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. A ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitored area affected by precipitation and other factors. This method is used to detect the change of the radar image before and after the landslide. The results show that the proposed method allows for easier clustering and segmenting, and the change detection results can significantly reduce the noise points while retaining the change area. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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