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.
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