Road Network Extraction from High Resolution SAR
Automatic road network extraction from satellite images is currently considered to be an important research trend in the field of remote sensing and photogrammetry. This paper presents a method for automatic extraction of road networks from synthetic aperture radar (SAR) imagery. The method consists of three steps. During the first step, road area candidates are detected based on the fusion of extracted features: minimum radiance, contrast, and direction of minimum radiance. In the second step, several refinement criteria are used in order to discard detected false candidates, and a thinning morphology operator is applied on the road areas to extract the road segments. Seed points of interest are then extracted to using in a network snake model, which is employed in the third step to connect the seed points in order to form the road network. Minimizing the energy function of the Snake model and then using a perceptual grouping algorithm is for discarding redundant segments and avoiding gaps between segments is used sequentially in the network snake model. The proposed algorithm is tested on TerraSAR X images with different areas. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality. An accuracy assessment showed that the proposed model is capable of achieving a quality index between 56 to 82.5 percent. Introduction As an essential part of a modern transportation system, road network extraction has attracted increasing interest in practical applications (Maboudi et al. 2016) Road network extraction is not only scientifically challenging but is also of major practical importance in military and civilian areas, e.g. map updating (Mena, 2003; Nakaguro et al. 2011; Sun et al. 2014) navigation (Matkan et al. 2014) and urban evolution monitoring, as well as for disaster relief and response (Chen et al. 2008) When handling these applications, the road position first needs to be determined before follow up functions can proceed. Furthermore, in image processing and remote sensing, the road feature is the foundation of many complex tasks, such as image registration (Dell'Acqua et al. 2004) and urban block outlining (Dell'Acqua et al. 2009) Today, high resolution radar and optical images are used in a wide variety of applications specifically for automatic extraction of road networks. Motivating factors for using radar images include all weather imaging capability and day/night data acquisition (Tupin et al. 1998) To manage the automatic road extraction task, three levels of analysis should be considered: (a) Low level road feature extraction, which aims to extract information indicating the likelihood of a particular pixel being located on a road segment. This level generally includes the following methods: line detectors (Tupin et al. 1998) classificationbased methods (Dell'Acqua and Gamba, 2001) and template matching (Cheng et al. 2011) (b) Medium level road segment extraction, in which local properties based on the output of the extracted road features are tested to produce qualified segments. Methods include the threshold based technique (Tupin et al. 1998) and Bayesian analysis (Stilla and Hedman, 2010) This procedure can be skipped in a direct transition from low level to high level processing. (c) High level road network construction, which aims to fill the gaps between the road segments, discards any false segments, and connects the road candidate portions from a global perspective. Common approaches at this level include Markov random field (MRF) models (Tupin et al. 1998) graph based method (Gerke and Heipke, 2008) methods based on the active contour model (ACM) (Bentabet et al. 2003; Leninisha and Vani, 2015; Liu et al. 2013) the region growing based method (Baumgartner et al. 1999; Lu et al. 2014) dynamic programming (Dell'Acqua and Gamba, 2001) the genetic algorithm (GA) (Jeon et al. 2002) and particle filtering (Liu et al. 2013) The ACM is widely used to extract regions of interest objects (Niu, 2006) Kass et al. (1988) introduced the snake active contour model, in which the model performance depends upon an initial contour drawn close to the edge of the object of interest. Subsequently, various approaches have been introduced based on the ACM. The balloon model proposed by Cohen (1991) and Unsalan and Sirmacek (2012) expands an initially defined region until it reaches the boundary of a road. Likewise, the geodesic active contour model (Caselles et al. 1997; Mssedi et al. 2011) region growing techniques based on the ACM (Amo et al. 2006) and the network snake method (Butenuth and Heipke, 2012) have been introduced for road extraction or updating the existing road database. It is important to note that (Butenuth and Heipke, 2012) updated the road vectors using airborne SAR image as well as the existing road database, which was used as the initial value for the network snake model. The topological properties of the road database are preserved exactly and no change is possible whereas the existing road database (not automatically detected) may contain surplus segments. To obtain an accurate and precise road network based on snake models, automatically extracted initial values are required. In this study, the initial seed point values for each snake are automatically extracted from SAR images and then the road network is extracted. The following steps are used for road network extraction from high resolution SAR images: 1. Road Area Detection: Features describing road properties in high resolution SAR images are extracted and fused based on a fuzzy algorithm to detect the road areas. 2. Refinement: Several spatial and spectral criteria are taken into account to refine the detected road areas. The Department of Remote Sensing Engineering, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran. (msaati@ut.ac.ir) Photogrammetric Engineering Remote Sensing Vol. 83, No. 3, March 2017, pp. 25 xxx. 0099 1112/17/25 xxx (c) 2017 American Society for Photogrammetry and Remote Sensing doi: 10.14358/PERS.83.3.87 PHOTOGRAMMETRIC ENGINEERING REMOTE SENSING March 2017 25 3. Vectorization of Road Network: Thinning of the extracted road areas is performed. In this process, a morphological thinning operator is first implemented and pruned; the junction points are then extracted, and finally, a piecewise straight line approximation is used to define the initial seed points for each snake segment. By tracking the extracted seed points as the initial points of each snake segment and considering the junction points, the network snake model is formed. The road network is extracted by minimizing the energy function of the snake model and some properties of perceptual grouping algorithm for discarding redundant segments and avoids gaps between segments. In the remainder of this paper, the materials and methods are documented in the next Section, followed by the results. The final section concludes the paper and provides some suggestions for the future studies. Materials and Methods The procedure proposed in this paper is divided into three steps, explained in the previous section and shown in Figure 1. The first step is the road area detection. This step is based on the road candidate extraction procedure proposed by (Negri et al. 2006) and developed by (Saati and Amini, 2015; Saati et al. 2015) for SAR data sets recorded in urban areas. The second step is the refinement of road area. This step contains filling the gaps between some detected road areas and discarding false areas, and finally third step is the vectorization of the road network as a main contribution of this study. Road Area Detection This step of the proposed framework is referred to as Road Area Detection. This procedure is not modified and is briefly recalled here for the sake of clarity. In summary, for the extraction of road pixels, the first step is to calculate a number of spatial features in a square window around the central pixel (i,j) with radiance p(i,j) Each of these features will be a function of the window size (R x R) The sum of the radiance in a specific direction of th in the selected window is termed the total radiance (r) and can be given in Equation 1 (Negri et al. 2006) r i j R p i k j k k R k R cos sin th th th 2 2