An Extended Random Walker Approach for Object Extraction by Integrating VGI Data and VHR Image

Abstract

Automatic extraction of objects in urban areas from very-high-resolution (VHR) images is of great significance to many applications. Existing approaches consider little information on spatial relationships, backgrounds, and prior knowledge of target objects, leading to that they did not perform well in object extraction. Now free and fast growing volunteered geographic information (VGI) can be accessed easily; thus, they can be used as prior information to improve the performance. This study develops an extended random walker (RW) approach to form a bottom-up and top-down mechanism for extracting target objects by combining VHR images and VGI data. Novel aspects of our approach include: 1) both the shape and spectral prior terms are incorporated into the extended RW algorithm; 2) an end-to-end framework is proposed to automatically select both foreground and background seeds with the assistance of VGI data; and 3) the shape prior of VGI data provides top-down information to select background and foreground seeds and help fuse bottom-up image information (i.e., foreground and background seeds and spatial relationships) to extract target objects. The extended RW approach was validated on building and lake datasets, and its performance is evaluated on both pixel and object levels. Quantitative comparisons with the original RW and random forest (RF) algorithm indicate that the proposed approach achieves significant better performance. Besides, it can successfully extract the partly occluded buildings.