Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Rong Gui,Chao Song,Fangling Pu,Xin Xu,Hao Dong

doi:10.3390/rs8090708

Abstract

Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

Highlights

With a significant amount of data available from the new very high resolution (VHR) Synthetic aperture radar (SAR) sensors, for example Cosmo-SkyMed and TerraSAR-X [1], more detailed urban information can be employed for various important application scenarios, such as the monitoring of changes in urban areas and assessing natural disasters [2,3]
The experiment results on real TerraSAR-X images show that the proposed method can from VHR SAR images, aiming to overcome the heterogeneity and complexity of various building overcome the diversities of building orientations, building sizes and shapes, reaching relatively appearances
We highlight the problem of extracting individual buildings from SAR images under a semantic expression framework

Summary

Introduction

With a significant amount of data available from the new very high resolution (VHR) SAR sensors, for example Cosmo-SkyMed and TerraSAR-X [1], more detailed urban information can be employed for various important application scenarios, such as the monitoring of changes in urban areas and assessing natural disasters [2,3]. Building extraction is a key step in urban information analysis from. SAR images contain important information that is complementary to other sensor data [5]. Extracting spatial and geometric structure information of buildings from VHR SAR images is a highly attractive problem [2,6]. Complex environmental factors [4,7], different building orientations [4,8] and their heterogeneity [9], speckle [10], and acquisition geometry, made building extraction from SAR data an open challenge [2,11]

Methods

Results

Discussion

Conclusion