Abstract
Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm.
Highlights
Introduction iationsAutomatic building change detection using remote sensing imagery is significant for city monitoring, disaster assessment, map updating, etc
For the very high resolution (VHR) satellite remote sensing imagery, the spatial resolution is better than 1 m, which brings the complex details of ground features
The spectral change feature is extracted through morphological building index (MBI) [40,41] difference, and the height change feature is extracted through the robust height difference of normalized digital surface models (nDSM)
Summary
Introduction iationsAutomatic building change detection using remote sensing imagery is significant for city monitoring, disaster assessment, map updating, etc. For the very high resolution (VHR) satellite remote sensing imagery, the spatial resolution is better than 1 m, which brings the complex details of ground features. It makes the results of two-dimensional (2D) change detection vulnerable to minor changes on the ground surface. With the development of dense stereo image matching technology [1,2,3] and the launch of more and more VHR stereo observation satellites, we can extract accurate height information from the satellite stereo images, which is very useful for building change detection. The introduction of the height features makes three-dimensional (3D) change detection insensitive to the image’s detailed
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