CHANGE DETECTION WITH MULTI-SOURCE DEFECTIVE REMOTE SENSING IMAGES BASED ON EVIDENTIAL FUSION
Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the proposed method, post-classification comparis...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/125/2016/isprs-annals-III-7-125-2016.pdf |
| Summary: | Remote sensing images with clouds, shadows or stripes are usually considered as defective data which limit their application for
change detection. This paper proposes a method to fuse a series of defective images as evidences for change detection. In the
proposed method, post-classification comparison process is firstly performed on multi-source defective images. Then, the
classification results of all the images, together with their corresponding confusion matrixes are used to calculate the Basic Belief
Assignment (BBA) of each pixel. Further, based on the principle of Dempster-Shafer evidence theory, a BBA redistribution process
is introduced to deal with the defective parts of multi-source data. At last, evidential fusion and decision making rules are applied on
the pixel level, and the final map of change detection can be derived. The proposed method can finish change detection with data
fusion and image completion in one integrated process, which makes use of the complementary and redundant information from the
input images. The method is applied to a case study of landslide barrier lake formed in Aug. 3rd, 2014, with a series of multispectral
images from different sensors of GF-1 satellite. Result shows that the proposed method can not only complete the defective
parts of the input images, but also provide better change detection accuracy than post-classification comparison method with single
pair of pre- and post-change images. Subsequent analysis indicates that high conflict degree between evidences is the main source of
errors in the result. Finally, some possible reasons that result in evidence conflict on the pixel level are analysed. |
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| ISSN: | 2194-9042 2194-9050 |