Joint Probability Space Based Self-adaptive Remote Sensing Change Detection Method
A variety of factors has led to radiometric variations of the land cover, which severely limits the threshold based change detection method performance. To overcome this problem, we propose a joint probability density space based self adaptive multi-threshold change detection approach. Firstly, the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Surveying and Mapping Press
2016-01-01
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| Series: | Acta Geodaetica et Cartographica Sinica |
| Subjects: | |
| Online Access: | http://html.rhhz.net/CHXB/html/2016-1-73.htm |
| Summary: | A variety of factors has led to radiometric variations of the land cover, which severely limits the threshold based change detection method performance. To overcome this problem, we propose a joint probability density space based self adaptive multi-threshold change detection approach. Firstly, the two images of the same geographic area acquired at different time are transformed into the joint probability space. In which, the land cover change pixels are defined as outliers and identified by an iterative method. Then, the extracted outliers are mapped back to the original image space and determine the change area. To illustrate the performance of the proposed method, an experimental analysis on two classical applications is reported and discussed, results show that the proposed method over performed the state of art method in true rate, false alarm rate and omit alarm rate, with high stability. |
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| ISSN: | 1001-1595 1001-1595 |