Modified S 2 CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

Honglyun Park, Jaewan Choi, Wanyong Park and Hyunchun Park
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Honglyun Park: Department of Civil Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju Chungbuk 28644, Korea
Jaewan Choi: Department of Civil Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju Chungbuk 28644, Korea
Wanyong Park: Agency for Defense Development, Yuseong-gu, Daejeon 34186, Korea
Hyunchun Park: Agency for Defense Development, Yuseong-gu, Daejeon 34186, Korea

Sustainability, 2018, vol. 10, issue 9, 1-20

Abstract: This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S 2 CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S 2 CVA was used to minimize the false alarm rate when applying S 2 CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S 2 CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S 2 CVA.

Keywords: KOMPSAT-2; cross-sharpening; multitemporal satellite images; sequential spectral change vector analysis (S 2 CVA); change detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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