Object-Based Change Detection of Very High Resolution Satellite Imagery Using the Cross-Sharpening of Multitemporal Data

In this letter, we present a method for unsupervised change detection based on the cross-sharpening of multitemporal images and image segmentation. Our method effectively reduces the change detection errors caused by relief or spatial displacement between multitemporal images with different acquisit...

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Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 12; no. 5; pp. 1151 - 1155
Main Authors Wang, Biao, Choi, Seokkeun, Byun, Younggi, Lee, Soungki, Choi, Jaewan
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.05.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Change detection algorithms
Correlation
Cross-sharpening
Image resolution
Image segmentation
Indexes
Remote sensing
Satellites
spatial displacement
unsupervised change detection
unsupervised change detection
Cross-sharpening
image segmentation
spatial displacement
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Summary:In this letter, we present a method for unsupervised change detection based on the cross-sharpening of multitemporal images and image segmentation. Our method effectively reduces the change detection errors caused by relief or spatial displacement between multitemporal images with different acquisition angles. A total of four cross-sharpened images, including two general pansharpened images, were generated. Then, two pairs of cross-sharpened images were analyzed using change detection indexes. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2014.2386878