Abstract
Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.
Highlights
We focus on local land cover change events in the real world and propose an adaptive contextual information-based Land cover change detection (LCCD) approach which extracts contextual information adaptively around a central pixel and computes the change magnitude between central pixels based on the distance between adaptive extended regions
It is worth noting that advantage of the proposed strategy for generating change magnitude image lies in the following aspects: (1) since the shape and size of the extended region is adaptive, the pixels within an adaptive region give a higher similarity in spectra, it is more objective than considering the contextual information through a regular window or a mathematical model; (2) Based on the constraints of the two parameters T1 and T2, the extension of a region around a pixel is self-adaptive and the mean value of the pixels within an extended region is used to measure the change magnitude between the pairwise pixel
To present the meaning of these indices, we defined UC as the number of change pixels that are unchanged pixels in binary change detention map (BCDM) when compared with the ground reference, TRU is the number of pixels that are unchanged pixels in the ground reference, CU is the unchanged pixels in the BCDM but is changed pixels in the ground reference, TRC is the total number of changed pixels in the ground reference truth
Summary
Land cover change detection (LCCD), which is a classic problem, has recently been a hot topic in remote sensing [1,2,3,4,5,6,7,8], because LCCD plays an increasingly important role in making decisions to promote sustainable urban development, such as urban expansion [9,10], city temperature change analysis [11,12], urban air quality analysis [13], man-made target change detection [14], and urban landscape change [15,16,17], etc. Examples of such approaches are level set evolution with local uncertainty constraints (LSELUC) [45] and the multiresolution level set (MLS) [46]. To evaluate the accuracy and performance of the proposed approach, it is applied to three real land cover change events using bi-temporal remote sensing images and compares the results with four widely used contextual-based methods, i.e., LSELUC [45], MLS [46], CVA [36], and PCA_Kmeans [43].
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