Application of IR-MAD using synthetically fused images for change detection in hyperspectral data

Biao Wang, Seok Keun Choi, You Kyung Han, Soung Ki Lee, Jae Wan Choi

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)578-586
Number of pages9
JournalRemote Sensing Letters
Volume6
Issue number8
DOIs
StatePublished - 3 Aug 2015

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