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Remote Sens. 2015, 7(8), 10347-10363;

Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Core Technology Research Laboratory, Pixoneer Geomatics, Daejeon 305-733, Korea
Center for Information and Communication Technology, Fondazione Bruno Kessler, Via Sommarive, 18-38123 Povo, Trento, Italy
Satellite Information Research Laboratory, Korea Aerospace Research Institute, Daejeon 305-333, Korea
Author to whom correspondence should be addressed.
Academic Editors: Guy J-P. Schumann, Ioannis Gitas and Prasad S. Thenkabail
Received: 18 February 2015 / Revised: 3 August 2015 / Accepted: 4 August 2015 / Published: 12 August 2015
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In this paper, a novel unsupervised change detection approach based on image fusion is introduced. The approach aims to extract the reliable flood extent from very high-resolution (VHR) bi-temporal images. The method takes an advantage of the spectral distortion that occurs during image fusion process to detect the change areas by flood. To this end, a change candidate image is extracted from the fused image generated with bi-temporal images by considering a local spectral distortion. This can be done by employing a universal image quality index (UIQI), which is a measure for local evaluation of spectral distortion. The decision threshold for the determination of changed pixels is set by applying a probability mixture model to the change candidate image based on expectation maximization (EM) algorithm. We used bi-temporal KOMPSAT-2 satellite images to detect the flooded area in the city of N′djamena in Chad. The performance of the proposed method was visually and quantitatively compared with existing change detection methods. The results showed that the proposed method achieved an overall accuracy (OA = 75.04) close to that of the support vector machine (SVM)-based supervised change detection method. Moreover, the proposed method showed a better performance in differentiating the flooded area and the permanent water body compared to the existing change detection methods. View Full-Text
Keywords: flood extent; change detection; spectral distortion; KOMPSAT-2 satellite imagery flood extent; change detection; spectral distortion; KOMPSAT-2 satellite imagery

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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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Byun, Y.; Han, Y.; Chae, T. Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images. Remote Sens. 2015, 7, 10347-10363.

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