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Remote Sens. 2018, 10(3), 472; https://doi.org/10.3390/rs10030472

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

1
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
2
College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China
3
Key Laboratory of Geospatial Big Data Mining and Application, Hunan Province, Changsha 410081, China
4
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
5
School of remote sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 21 February 2018 / Accepted: 14 March 2018 / Published: 17 March 2018
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Abstract

In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images. View Full-Text
Keywords: land cover change detection (LCCD); very high-spatial resolution remote sensing images; multi-scale segmentation; expectation maximum (EM) land cover change detection (LCCD); very high-spatial resolution remote sensing images; multi-scale segmentation; expectation maximum (EM)
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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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Lv, Z.; Liu, T.; Wan, Y.; Benediktsson, J.A.; Zhang, X. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sens. 2018, 10, 472.

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