Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images

Abstract

Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-the-art methods in terms of visual performance and quantitative measurements.