Abstract
Semantic and instance segmentation methods are commonly used to build extraction from high-resolution images. The semantic segmentation method involves assigning a class label to each pixel in the image, thus ignoring the geometry of the building rooftop, which results in irregular shapes of the rooftop edges. As for instance segmentation, there is a strong assumption within this method that there exists only one outline polygon along the rooftop boundary. In this paper, we present a novel method to sequentially delineate exterior and interior contours of rooftops with holes from VHR aerial images, where most of the buildings have holes, by integrating semantic segmentation and polygon delineation. Specifically, semantic segmentation from the Mask R-CNN is used as a prior for hole detection. Then, the holes are used as objects for generating the internal contours of the rooftop. The external and internal contours of the rooftop are inferred separately using a convolutional recurrent neural network. Experimental results showed that the proposed method can effectively delineate the rooftops with both one and multiple polygons and outperform state-of-the-art methods in terms of the visual results and six statistical indicators, including IoU, OA, F1, BoundF, RE and Hd.
Highlights
Mapping the distribution of buildings using remote sensing imagery is of great significance in land use management, urban planning, disaster emergency response, and more
Since convolutional neural networks (CNN) have achieved excellent performance in the field of computer vision [1], CNN-based deep learning methods have been widely applied to building extraction tasks from Very High Resolution (VHR) images [2,3,4,5,6,7,8] for: locating buildings using object detection [9]; the pixel-level classification of buildings using semantic segmentation [10]; and the delineation of rooftop outlines using instance segmentation [11]
We propose a novel instance segmentation approach to delineate the boundary of rooftops with or without holes; 2
Summary
Mapping the distribution of buildings using remote sensing imagery is of great significance in land use management, urban planning, disaster emergency response, and more. Object detection methods are often applied in building localization tasks. In rural areas where buildings are sparse, a two-stage CNN [12] structure can be used to improve the accuracy of building localization by first performing village detection and building detection. This method only outputs the coordinate position of the external rectangle where the building is and cannot reflect the geometry of the building rooftops.
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