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Sustainability 2018, 10(9), 3301; https://doi.org/10.3390/su10093301

Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

1
Department of Civil Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju Chungbuk 28644, Korea
2
Agency for Defense Development, Yuseong-gu, Daejeon 34186, Korea
*
Author to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 11 September 2018 / Accepted: 13 September 2018 / Published: 15 September 2018
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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 (S2CVA), 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 S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA 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 S2CVA. View Full-Text
Keywords: KOMPSAT-2; cross-sharpening; multitemporal satellite images; sequential spectral change vector analysis (S2CVA); change detection KOMPSAT-2; cross-sharpening; multitemporal satellite images; sequential spectral change vector analysis (S2CVA); change detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Park, H.; Choi, J.; Park, W.; Park, H. Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection. Sustainability 2018, 10, 3301.

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