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The automatic vectorization of building shape from very high resolution (VHR) remote sensing imagery is fundamental in many fields such as urban management and geodatabase updating. Recently, deep convolutional neural networks (DCNNs) have been successfully used for building edge detection but the results are raster images rather than vectorized maps, and do not meet the requirements of many applications. Although there are some algorithms for converting raster images into vector maps, such vector maps often have too many vector points and irregular shapes. This study proposed a building shape vectorization hierarchy, which combined DCNNs-based building edge detection and a corner extraction algorithm based on principal component analysis (PCA) for rapidly extracting building corners from the building edges. Experiments on the Jiangbei New Area Buildings and Massachusetts Buildings datasets showed that compared with the state-of-the-art corner detectors, the building vector corners extracted using our proposed algorithm had fewer breakpoints and isolated points, and our building vector boundaries were more complete and regular. In addition, the building shapes extracted using our hierarchy were 7.94&#x0025; higher than the non-maximum suppression (NMS) method in terms of relaxed overall accuracy (OA) on the Massachusetts dataset. Overall, our proposed hierarchy is effective for building shape vectorization.

A Building Shape Vectorization Hierarchy from VHR Remote Sensing Imagery Combined DCNNs-based Edge Detection and PCA-based Corner Extraction