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Remote Sens. 2017, 9(8), 804; https://doi.org/10.3390/rs9080804

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

1
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
2
Anhui Key Laboratory of Smart City and Geographical Condition Monitoring, Hefei 230031, Anhui, China
3
School of Civil Engineering, Chungbuk National University, Cheongju 361763, Chungbuk, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: George P. Petropoulos, James Campbell and Prasad Thenkabail
Received: 17 June 2017 / Revised: 19 July 2017 / Accepted: 4 August 2017 / Published: 5 August 2017
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Abstract

Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation. View Full-Text
Keywords: change detection; high resolution image; image fusion; land cover; image analysis change detection; high resolution image; image fusion; land cover; image analysis
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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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Wang, B.; Choi, J.; Choi, S.; Lee, S.; Wu, P.; Gao, Y. Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images. Remote Sens. 2017, 9, 804.

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