Automatic Vectorization Extraction of Flat-Roofed Houses Using High-Resolution Remote Sensing Images

The vectorization of buildings provides quantitative disaster information and building damage extraction for the accurate assessment of disasters. This study presents an automatic extraction technology for flat-roofed houses by using high-resolution remote sensing images based on the single-point extraction of such houses and the concept of stepwise image segmentation. Vegetation removal, multi-scale segmentation, k-means clustering segmentation, rectangle recognition, and morphological processing are adopted to identify the center of an assumed house in the image by considering the characteristics of remote sensing images and the main features of house recognition (i.e., spectral characteristics and shape features). Furthermore, the single-point extraction of flat-roofed houses is used to acquire the surface vector diagram of the extracted houses, and finally, achieve the automatic extraction of flat-roofed houses. Experimental result shows that the accuracy of the vectorization extraction of flat-roofed houses through the combination is 83.33%.

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