Abstract
Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.
Highlights
Building change detection, which is the process of identifying changed buildings through comparison and analysis of bi-temporal or multi-temporal high-resolution remote sensing data, plays an important role in geospatial information services including urban village renovation, identification of illegal or unauthorized buildings, and monitoring of urban growth
A novel building change detection framework based on co-segmentation and superpixel-based graph cuts from bi-temporal digital surface models and aerial images is proposed in this paper
Bi-temporal superpixels are first obtained by the co-segmentation of bi-temporal digital surface models (DSMs) with the simple linear iterative clustering (SLIC) algorithm
Summary
Building change detection, which is the process of identifying changed buildings through comparison and analysis of bi-temporal or multi-temporal high-resolution remote sensing data, plays an important role in geospatial information services including urban village renovation, identification of illegal or unauthorized buildings, and monitoring of urban growth. Some scholars [1,2,3,4,5,6,7,8] proposed detecting building changes from bi-temporal or multi-temporal high-resolution remote sensing imagery on the basis of spectral information alone. In terms of geometric comparison, some scholars proposed detecting building changes with height differencing [11,12,13,14] and projection-based differences [15]. In such methods, DSM is generally derived from an airborne laser scanner (ALS). Due to the high cost of acquiring point cloud data by ALS, in most cases, there are no appropriate bi-temporal or multi-temporal ALS point cloud data available for building change detection, to some extent limiting the practicality of such methods
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