Unsupervised Change Detection in Multispectral Remotely Sensed Imagery With Level Set Methods

被引:152
作者
Bazi, Yakoub [1 ]
Melgani, Farid [2 ]
Al-Sharari, Hamed D. [3 ]
机构
[1] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11421, Saudi Arabia
[2] Univ Trento, Dept Informat Engn & Comp Sci, I-38050 Trento, Italy
[3] Al Jouf Univ, Coll Engn, Al Jouf 2014, Saudi Arabia
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2010年 / 48卷 / 08期
关键词
Active contour; image segmentation; level set method; multiresolution analysis; unsupervised change detection;
D O I
10.1109/TGRS.2010.2045506
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In this paper, the unsupervised change-detection problem in remote sensing images is formulated as a segmentation issue where the discrimination between changed and unchanged classes in the difference image is achieved by defining a proper energy functional. The minimization of this functional is carried out by means of a level set method which iteratively seeks to find a global optimal contour splitting the image into two mutually exclusive regions associated with changed and unchanged classes, respectively. In order to increase the robustness of the method to noise and to the choice of the initial contour, a multiresolution implementation, which performs an analysis of the difference image at different resolution levels, is proposed. The experimental results obtained on three different multitemporal remote sensing images acquired by low- as well as high-spatial-resolution optical remote sensing sensors suggest a clear superiority of the proposed approach compared with state-of-the-art change-detection methods.
引用
收藏
页码:3178 / 3187
页数:10
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