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

Jinxing Chen,Chao Wang,Hong Zhang,Fan Wu,Wanming Lei,Bo Zhang

doi:10.3390/rs9030263

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.

Highlights

Synthetic aperture radar (SAR) sensors with high resolution, either airborne or spaceborne, show high potential in applications of urban remote sensing such as damage assessment [1,2,3], urban environment monitoring [4], and three-dimensional (3-D) reconstruction [5]
Building roofs are extracted accurately and a detection rate of ~86% is achieved on a complex synthetic aperture radar (SAR) scene
The results show a high detection rate, but the building outlines are inaccurate

Summary

Introduction

Synthetic aperture radar (SAR) sensors with high resolution, either airborne or spaceborne, show high potential in applications of urban remote sensing such as damage assessment [1,2,3], urban environment monitoring [4], and three-dimensional (3-D) reconstruction [5]. As one of the most important components in urban areas, buildings attract more attention for urban studies. Many techniques for building detection from VHR SAR images have been developed. These methods can be divided into two categories according to the types of experimental data. The first category relies on the availability of ancillary or multiple image data, e.g., multidimensional data [5], multitemporal data [6], InSAR [7], and PolSAR [8], which implies that the area under investigation is imaged more than once with different viewing configurations (changed incidence and/or aspect angle)

Methods

Findings

Discussion

Conclusion