Exploiting Multi-Aspect SAR Data for Object Extraction

In this paper we describe a fusion approach for automatic object extraction from multi-aspect SAR images. Before fusion the uncertainty of each extracted object is assessed by means of Bayesian probability theory. The assessment is performed on attribute-level and is based on predefined probability density functions learned from training data. I. INTRODUCTION Automatic extraction of man-made objects from synthetic aperture radar (SAR) images is regarded as a complicated task. Compared to optical image acquisition, SAR system is an active system and can operate during day and night. It is also nearly weather-independent and, moreover, during bad weather conditions, SAR is the only operational system available today. Extraction of man-made objects from SAR images therefore offers a suitable complement or alternative to object extraction from optical images. The recent development of new high resolution SAR systems offers new potential for automatic object extraction. Satellite SAR images up to 1 m resolution will soon be available by the launch of the German satellite TerraSAR-X (1). Airborne images already provide resolution up to 1 decimetre (2). However, the improved resolution does not automatically make automatic object extraction easier, yet it faces new challenges. Especially in urban areas, the complexity arises through dominant scattering caused by building structures, traffic signs and metallic objects in cities. These bright features hinder important extractable features. The inevitable consequences of the side-looking geometry of SAR, occlusions caused by shadow- and layover effects, is present in forestry areas as well as in built-up areas. In urban areas, the best results for the visibility of roads are obtained, when the illumination direction coincide with the main road orientations (3). Preliminary work has shown that the usage of SAR images illuminated from different directions (i.e. multi- aspect images) improves the road extraction results. This has been tested both for real and simulated SAR scenes (4)(5). Multi-aspect SAR images has appeared to be an interesting topic for automatic building extraction as well (6). In this article we present a fusion concept for object extraction based on a Bayesian statistical approach, which incorporates both global context and sensor geometry. The fusion will be implemented in a road extraction approach, (Sect. II), but can as well be applied for other man-made objects. The main focus of this paper is the proposed fusion module, which is explained in Sect. III. Some intermediate results of an uncertainty assessment of line segments based on a training step and global context are discussed in Sect IV. II. ROAD EXTRACTION SYSTEM The extraction of roads from SAR images is based on an already existing road extraction approach (7), which was originally designed for optical images with a ground pixel size of about 2m (8). The first step consists of line extraction using Steger's differential geometry approach (9), which is followed by a smoothening and splitting step. By applying explicit knowledge about roads, the line segments are evaluated according to their attributes such as width, length, curvature, etc. The evaluation is performed within the fuzzy theory. A weighted graph of the evaluated road segments is constructed. For the extraction of the roads from the graph, supplementary road segments are introduced and seed points are defined. Best- valued road segments serve as seed points, which are connected by an optimal path search through the graph. The novelty presented in this paper refers on one hand to the adoption of the fusion module to multi-aspect SAR images and on the other hand to a probabilistic formulation of the fusion problem instead of using fuzzy-functions.