期刊
REMOTE SENSING LETTERS
卷 6, 期 8, 页码 578-586出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/2150704X.2015.1062155
关键词
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资金
- Space Core Technology Development Program through the National Research Foundation of Korea (NRF)
- Ministry of Science, ICT & Future Planning [NRF-2012M1A3A3A02033469, NRF-2014M1A3A3A03034798]
- National Research Foundation of Korea [2014M1A3A3A03034798] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The main objective of this letter is to improve the accuracy of unsupervised change detection method and minimize registration errors among multi-temporal images in the change detection process. To this end, iteratively regularized multivariate alteration detection (IR-MAD) is applied to synthetically fused images. First, four synthetically fused hyperspectral images are generated using the block-based fusion method. Then, the IR-MAD is applied to three pairs of the fused images using integrated IR-MAD variates, to decrease the falsely detected changes. To focus on the mis-registration effects, we apply the method to both a correctly registered data-set and a data-set with deliberately misaligned images. In this experiment using multi-temporal Hyperion images, the changed areas are more efficiently detected by our method than by the original IR-MAD algorithm.
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