Land cover change detection (LCCD) with very high-resolution (VHR) remote sensing images has been widely used in various applications. However, pseudo-changes and noise usually affect the performance of detection map. In this letter, an object-oriented sorted-histogram similarity measurement (OSSM) is proposed for measuring the change magnitude between bioral remote sensing images. First, multi-scale objects are acquired for the post-event image using a multi-scale segmentation algorithm, and then the pixels within each object are considered to construct the pairwise histograms and the bin of each histogram is sorted in descending order. Second, a bin-to-bin (B2B) distance is defined to measure the change magnitude between the pairwise object-based histograms, and the change magnitude image (CMI) is generated after all the bioral images are scanned object by object. Finally, a simple yet effective method called Otsu is used to divide the CMI into binary change detection maps. The experiments on three pairs of VHR images produced promising results compared with five popular LCCD approaches, for example, the improvement is about 2.5% for F-score.
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© 2004-2012 IEEE.
- Land cover change detection (LCCD)
- multi-scale segmentation
- object-oriented sorted-histogram similarity measurement (OSSM)
- very high resolution (VHR) remote sensing images