Automatic Detection of Low-Rise Gable-Roof Building from Single Submeter SAR Images Based on Local Multilevel Segmentation

Abstract

Low-rise gable-roof buildings are a typical building type in shantytowns and rural areas of China. They exhibit fractured and complex features in synthetic aperture radar (SAR) images with submeter resolution. To automatically detect these buildings with their whole and accurate outlines in a single very high resolution (VHR) SAR image for mapping and monitoring with high accuracy, their dominant features, i.e., two adjacent parallelogram-like roof patches, are radiometrically and geometrically analyzed. Then, a method based on multilevel segmentation and multi-feature fusion is proposed. As the parallelogram-like patches usually exhibit long strip patterns, the building candidates are first located using long edge extraction. Then, a transition region (TR)-based multilevel segmentation with geometric and radiometric constraints is used to extract more accurate edge and roof patch features. Finally, individual buildings are identified based on the primitive combination and the local contrast. The effectiveness of the proposed approach is demonstrated by processing a complex 0.1 m resolution Chinese airborne SAR scene and a TerraSAR-X staring spotlight SAR scene with 0.23 m resolution in azimuth and 1.02 m resolution in range. Building roofs are extracted accurately and a detection rate of ~86% is achieved on a complex SAR scene.

DOI: 10.3390/rs9030263

Cite this paper

@article{Chen2017AutomaticDO, title={Automatic Detection of Low-Rise Gable-Roof Building from Single Submeter SAR Images Based on Local Multilevel Segmentation}, author={Jinxing Chen and Chao Wang and Hong Zhang and Fan Wu and Bo Zhang and Wanming Lei}, journal={Remote Sensing}, year={2017}, volume={9}, pages={263} }