P,.N.Anil et al. / International Journal of Engineering Science and Technology Vol. 2(3), 2010, 165-171
Automatic Road Extraction from High Resolution Imagery Based On Statistical Region Merging and Skeletonization P.N ANIL* and Dr. S. NATARAJAN ** * Asst. Professor, Dept. of Mathematics, Global Academy of Technology, Bangalore-98 and Research Scholar, Dr. MGR Educational and Research Institute, Chennai – 95, India Email:
[email protected] **
Professor, Dept. of Information Science & Engineering, PES Institute of Technology, Bangalore, India ABSTRACT
Road Extraction from high resolution imagery is of fundamental importance in the context of spatial data capturing and updating for GIS applications. This research work is an attempt to automate the process of extracting roads from high resolution imagery. In this work statistical region merging is used for image segmentation and road network is extracted based on skeleton pruning method based on contour partitioning, where the partitions are obtained by Discrete Curve Evolution. Keywords. Road extraction, Statistical Region Merging, Thresholding, Skeletonization.
1. INTRODUCTION Road extraction from remotely sensed data is a challenging issue in the field of photogrammetry and digital image processing. Extensive research has been done on road extraction from aerial and satellite imagery. The methods for road extraction can be mainly divided in to types, semi-automatic method and fully automatic method. Semi-automatic feature extraction is an interactive process between an operator and computer algorithms. In such methods an operator selects initial point(s) and a direction for road tracking algorithm. Gruen and Haihong [1] proposed a semi automatic road network extraction method based on dynamic programming. Mayer et al. [2] proposed a semi-automatic method for urban road extraction based on level set method by using data fusion of multi spectral and microwave radar images. In their paper the fast marching method of level set (LS-FM) is used as a tool to fuse different image features for road extraction. Seung and Taejung [3] proposed a semi automatic road extraction algorithm using template matching. In this method user needs to input an initial seed point to extract a road. Then the orientation of road seed is calculated automatically. They pointed out the method may not work on the road cast by shadow. Zhao et al. [4] proposed a semi automatic road extraction method using multi spectral high resolution satellite images. Firstly, ‘road mask’ was created by multi spectral data classification. Chains of edge pixels were tracked based on local edge direction and straight lines were obtained. Template matching was then used to determine the direction of line and to obtain next road node. A result in urban area was good for major roads whereas small roads were missed, as road boundaries were unclear due to the objects surrounding the roads. In rural area, both major and minor roads were properly extracted by indicating adequate control points. Keaton and Brokish [5] proposed a semi-automatic method for extracting roads from high resolution (1 meter) pan sharpened multispectral IKONOS imagery. In their method an operator provides an initial seed point on the road of interest, then the region is extracted using level set method. The new framework for semi-automatic feature extraction was proposed in [6] and applied to highway extraction and vehicle detection from multiple frame aerial photographs. The basis of new framework proposed in their work is a geometric deformable model. Peteri and Ranchin [7] proposed a
ISSN: 0975-5462
165
P,.N.Anil et al. / International Journal of Engineering Science and Technology Vol. 2(3), 2010, 165-171
method to extract the street network as surface elements from topologically correct graph using multi-resolution snakes. Automatic feature extraction has been always an interesting subject for researchers. In the recent years the automated extraction of roads has drawn the considerable attention due to the need for the efficient acquisition and updating of road data for geodatabases. Bacher and Mayer [8] presents an automatic approach for the extraction of roads from high resolution multispectral satellite imagery. Lines are extracted in all image channels and employed as initial road hypothesis as well as for the generation of training areas. The goal is to calculate membership value for the road class pixel. The assessment of road hypotheses is done based on geometrical and spectral properties by finding fuzzy values for the parameters length, average width and road energy. Road network is generated using weighted graph and detour factor to close small and large gaps respectively. Lin and Chen [9] proposed an automated image processing technique to extract control points for high spatial resolution satellite images which consists of two parts : Road extraction and Spot of road intersection searching. Jalal [10] proposed a method for extraction of main roads in SPOT panchromatic images. The method consists of three steps: feature extraction, fuzzy modeling and mathematical morphology. Kumagi et.al. [11] proposed the automatic extraction of roads based on area and road characteristics. In this research using high resolution data existing roads are extracted from the image and unwanted vegetation information is removed using NDVI values. Mohammadzadeh et.al. [12] proposed an algorithm to detect road network from high resolution image using combination of a developed fuzzy system and mathematical morphology. The algorithm in the mathematical morphology stage is based on the assumption that road network forms an elongated area which can be extracted as the connected components with certain criteria. Petari et.al. [13] developed a method in order to extract and characterize the road network form high resolution satellite images. The algorithm is divided into two modules: a topologically correct graph of road network is first extracted and roads are then extracted as surface elements. Doucette et.al.[14] presents a novel methodology for fully automated road centerline extraction, that exploits spectral content from high resolution multispectral images. Preliminary detection of candidate road centerline components is performed with Anti-parallel-edge Centerline Extraction (ACE). This is followed by constructing a road vector topology with a fuzzy grouping model that links nodes from a self-organized mapping of the ACE components. This paper presents a simple method for the automatic detection of roads from high resolution imagery. The paper proceeds as follows. In the first step image segmentation is performed using statistical region merging (SRM) followed by thresholding. In the next step new skeleton pruning by contour partitioning is used to extract the road network. 2. IMAGE SEGMENTATION USING STATISTICAL REGION MERGING Image segmentation is an essential and critical step in image processing. Image segmentation methods divide the image in to regions of coherent properties in an attempt to identify objects and their parts without the use of model of the object. Many image segmentation techniques are available in the literature. A review on image segmentation methods can be found in [15, 16]. A color image segmentation method based on fusion between edge pixels and region-growing images is presented in Dider and Vincent [17]. Image segmentation based on wavelets can be found in [18, 19] . Texture based image segmentation techniques are described in the literature [20, 21, 22, 23]. Image segmentation methods based on Markov Random Field (MRF) are presented in [24, 25, 26, 27]. Image segmentation based region growing and merging techniques can be found in [28, 29, 30]. In this paper we use statistical region merging technique for image segmentation proposed by Nock and Nielsen [31]. Statistical region merging is a linear-time fast and simple region growing segmentation algorithm based on an adaptive statistical threshold merging predicate on color channels that does not require to maintain dynamically the region adjacency graph. Algorithm: Let I be the input color image with |I| pixels, each containing the Red, Green and Blue values, each of the three belonging to the set {1, 2, 3, ………g}. Let I* denote the perfect scene (theoretical image) of I. In I*, the true or statistical regions represent theoretical objects sharing a common homogeneity property:
Inside any statistical region and given any color channel R, G , B , the statistical pixels have the same expectation for this color channel.
ISSN: 0975-5462
166
P,.N.Anil et al. / International Journal of Engineering Science and Technology Vol. 2(3), 2010, 165-171
The expectations of adjacent statistical regions are different for at least one color channel R, G , B .
The image I is obtained from I* by sampling each statistical pixel for observed RGB values. In each pixel of I*, each color channel is replaced by a set of exactly Q independent random variables, taking positive values on domains bounded by g/Q, such that any possible sum of outcomes of these Q random variables belongs to {1, 2, 3, …….g}. The parameter Q allows to quantify the statistical complexity of I*, the generality of the model and the statistical hardness of the task. The segmentation scheme is basically depends on merging predicate and an order to test region merging. The merging predicate for the RGB setting is given by:
true if a{R,G, B}, R'a Ra b(R) b(R) P(R, R) false otherwise (1) Here, Ra denotes the observed average for color channel a in region R and b(R) is given by:
b( R ) g
1 | R | ln |R| 2Q | R |
(2) g where 0 1 and | R|R| | | R | 1 . In 4-connexity, there are N
