 Open Access
 Total Downloads : 908
 Authors : Parul Malhotra, Saurabh Sharma
 Paper ID : IJERTV1IS5296
 Volume & Issue : Volume 01, Issue 05 (July 2012)
 Published (First Online): 02082012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Extraction of High Resolution Remote Sensing Image Features Using Triangulation Method
Parul Malhotra1
M.Tech Research Scholar, ECE Deptt.
Ambala College of Engg. & Applied Research (Kurukshetra University), Ambala
Saurabh Sharma2
M.Tech Research Scholar, CSE Deptt. MMEC (Maharishi Markandeshwar University Mullana), Ambala
Abstract
The project entitled Extraction of High Resolution Remote Sensing Image Features Using Triangulation Method plays an important role in extraction and updating of linear objects in cartographic processes. For extraction vectorization is performed using Constrained Delaunay Triangulation and triangles which are obtained from triangulation method are grouped into polygons that make up the vector image. The skeleton of these polygons represents the extracted linear structures which are obtained by using skeletonization. All works for this paper includes the following steps: Reading of image, Obtaining Correlation image, Obtaining vector image using Constrained Delaunay Triangulation (CDT) method, Canny edge detection, skeletonization to obtain morphological skeleton. The performance of the proposed algorithm is compared with the existing algorithm. The proposed algorithm is fast, automatic and has better results when tested.
Keywords Vectorization, Skeletonization, Remote Sensing Image, CDT.

Introduction
Road extraction is a fundamental step for the efficient uses of high resolution remote sensing images, such as updating GIS databases, registering image as references, mapping and so on. Besides, road extraction can also be helpful for the detection and identification of buildings, bridges, rivers and other ground features. It is rather hard to extract the different objects one by one and just do cost much time, and therefore, the extracting work can barely be set up in application. However, we all know that urban road is the main clue to analyze and interpret the city. So we can extract road information from remote sensing image first, and then analyze each district
segmented by roads. By doing this, the analyzing work will be easier than before. There have been many researches on the approaches of road extraction. These can be divided into the semiautomatic type and totally automatic type. Semiautomatic extraction for road feature takes advantage of manmachine interactive form to recognize the objects.
Traditional approaches on road extraction tend to extract the linear road. The accuracy is relatively low in extracting the urban road. There also exist omissions in some images because of the tiny, closed and preserved features. Sometimes the road in the image is close to the background, which could go against extraction. And a suitable algorithm for urban highways in modern days hasnt been applied in traditional approaches. Aiming to the shortfalls above, this paper proposes a new approach. That is road extraction based on Triangulation method. Not only be fit for the road extraction of general shapes, but also having certain superiority in the detection work of urban highways. The proposed method reduces various kinds of distortions and improves the Precision and Recall, compared with previous method.
The remainder of this paper is organized as follows. Section II briefly describes the general approach of the method. Section III describes the Proposed Algorithm. Section IV describes the experimental results which includes correlation, canny edge detection process, Triangulation and skeletonization which is the final step of the algorithm.

General Approach
Figure1 shows the flow diagram of our approach. First of all, reading of original image is done. After reading of image, preprocessing of image is done which includes correlation. Correlation refers to any of a broad class of statistical relationships involving dependence and dependence refers to any statistical
relationship between two random variables or two sets of data.
Read Original Image
Correlation Image
Canny Edge Detection
Grouping of Polygons Obtained from Constrained Delaunay Triangulation
Extraction of Skeleton of Obtained Polygon using Skeletonization
Figure 1. The Flow Diagram of Our Method
Canny Edge detection is performed to find the edges of the image. After detection of edges, vectorization is performed using Constrained Delaunay Triangulation and triangles which are obtained from triangulation method are grouped into polygons that make up the vector image. The final step in our algorithm is to extract the skeleton of the obtained polygons.

Proposed Algorithm

Original input image.
Read input image I. If image I is 24 bits, convert
to 8 bit grayscale image for edge detection.

Preprocessing.
Compute the correlation distance image. Compute the high correlation image.
To each pixel having a coefficient higher than a threshold t is assigned the gray level of the reference pixel. In the case where the current pixel has a low correlation coefficient, its spectral value is examined. If it has the same gray level
as the reference pixel, the point is deleted.

Canny edge detection.
Obtain all canny edge points as a binary image maskedge image. Include all boundary pixels of edge image as edge pixels. Delaunay triangulation of the contour set to obtain a list of triangles tessellating the regions between contours.
Obtain list of triples of indices of points
constituting triangles.

Extraction of edges that belong to two triangles.

Extraction of vectors from two triangles.
Both triangles have value either 0 or 1. If 1 comes then vectors are saved and if 0 comes then vectors are discarded in case of both triangles.

Thinning to find skeleton.
Thinning operation is applied on vectors to find skeleton.

Experimental Results

Correlation
Correlation is a broad class of statistical relationships between two or more random variables or observed data values. Correlation coefficients can range from 1 to +1. The value of 1 represents a perfect negative correlation while a value of +1 represents a perfect positive correlation. A value of 0 represents a lack of correlation.
The correlation coefficient is defined as:
i i i i
Corr x, y y y * x x / y y 2 * x x 2
where x: current pixel
y: reference pixel
Figure 2. The Original Image
Figure2 shows the original image on which whole process is to be taken.
Figure 3. Correlation Distance Image
Figure3 shows the correlation distance image. To each pixel having a coefficient higher than a threshold t is assigned the gray level of the reference pixel.
Figure4 shows the high correlation image. Computation of high correlation image is as follows:
Input: Original image I, reference pixel y for every pixel x
corrImg(x) = corr(x,y) end
I = grayscale(I) for every pixel x
if corr Img(x) > t highCorr(x) = I(x)
elseif (I(x) == I(y))
highCorr(x) = 0
The result of applying an edge detector to an image may lead to a set of connected curves that indicate the boundaries of objects, the boundaries of surface markings as well as curves that correspond to discontinuities in surface orientation. Thus, applying an edge detection algorithm to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preservin the important structural properties of an image. In this paper canny edge detection is applied at high correlation image. Figure5 shows the canny edge detection.
Figure 5. Canny Edge Detection
end end
Figure 4. High Correlation
Canny edge detection consists of following steps: Blurring of the image to remove noise.
The edges should be marked where the gradients of the image has large magnitudes.
Only local maxima should be marked as edges. Potential edges are determined by thresholding. Final edges are determined by suppressing all edges that are not connected to a very certain (strong) edge.
4.3. Triangulation
In the case where the current pixel has a low correlation coefficient, its spectral value is examined. If it has the same gray level as the reference pixel, the point is deleted. Thus the pixels that are highly correlated with the reference are emphasized.
4.2. Canny Edge Detection
Edge detection is important in image processing programs because it allows object separation and shape detection. Edge detection aims at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
The construction of vector image starts with Canny edge detection followed by a constrained Delaunay triangulation (CDT) where the lines resulting from the preprocessing stage are used as constraints for Delaunay triangulation. A triangulation of a set S of points on the plane presents a partition of an area, surrounded by a convex hull. Triangles are composed by vertices, given as an input. But if the triangles fulfill additional condition of an empty circumcircle, they present a Delaunay triangulation Let G be a straightline planar graph.
Figure 6. (a): A Planar StraightLine Graph G(S,Ec) (b): Conventional Delaunay Triangulation of the Point Set S (c): Ilustration of the Modified Circle Criterion for Constrained Delaunay Triangulation
A triangulation T is a constrained Delaunay triangulation (CDT) of G if each edge of G is an edge of T and for each remaining edge e of T there exists a circle C with the following properties: 1) The endpoints of edge e are on the boundary of C, and 2) if any vertex v of G is in the interior of C then it cannot be seen from at least one of the endpoints of e (i.e., if you draw the line segments from v to each endpoint of e then at least one of the line segments crosses an edge of G).
Figure7 shows the triangulation result. The presence of constraints in the CDT is ensured through local modifications where Delaunay edges are removed or flipped. CDTs solve the problem of enforcing boundary conformityensuring that triangulation edges cover the boundaries (both interior and exterior) of the domain being modeled.
Figure 7. Triangulation Result
4.4. Skeletonization
Skeletonization plays an important role in digital image processing especially for the analysis and recognition of binary images. Skeletonization is the process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground pixels. It is a global space domain technique for shape
representation. The skeleton typically emphasizes the geometric properties and topological shape, such as connectivity, topology, length, direction, and width. There are two wellknown paradigms for skeletonization methods: The first is iterative thinning of the original image until no pixel can be removed without altering the topological and morphological properties of the shape. The second definition used for a skeleton is that of the ridge lines formed by the centers of all maximal disks included in the original shape, connected to preserve connectivity. This leads directly to the use of distance transforms or similar measures which can be computed in only two passes on the image.
The final step in our algorithm is to extract the skeleton of the obtained polygons. Figure8 shows the skeletonization result. The skeleton represents the extracted lines.
Figure 8. Skeletonization Result


Accuracy
The proposed algorithm is fast and automatic with improved results when compared with previous results. To evaluate the obtained results we used the Precision and recall measures. Precision is defined as the number of relevant identified elements divided by the total number of retrieved elements, and Recall is defined as the number of relevant elements retrieved divided by the total number of existing relevant elements. The closer value to 1 indicates the better results.
In order to evaluate the performances of our algorithm, Recall and Precision have been calculated using the following formulas:
Precision = No. of relevant identified elements Total No. of retrieved elements
Recall = No. of relevant elements retrieved Total No. of existing relevant elements
We tested the algorithm on several high resolution satellite images containing road segment. The high
resolution satellite image taken in this paper had a precision of 0.87 and a recall value of 0.89.

Conclusion
This paper has realized features extraction from the high resolution remote sensing image, by the triangulation method. Our approach provides good recall and precision values when compared with previous methods. The algorithm is costly in terms of complexity and memory as it depends on the size of the CDT input set. The proposed work can also be extended for military purposes by carrying work on the region of interest.

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Parul Malhotra was born at H.No. 228/3 Jail Road,
Mandi(Himachal Pradesh) in India, on Sep. 13, 1988. She graduated from Indo Global College of Engg., Abhipur, Distt. Mohali (Punjab), India, and completed her Masters of Technology (batch 2009 11), ECE Deptt, from ACE&AR, Ambala (Haryana), INDIA. She
has successfully completed her Six Weeks Industrial training from (18 June 2007 to 30 July 2007) at BSNL, Bhiuli, Mandi (Himachal Pradesh), India. Also during her Six Month Industrial Project she worked on Fake Currency Detector from (1 July2008 to 31Dec 2008) at Central Scientific Instruments Organization (CSIO), Sector30C, Chandigarh 160030, India.
Saurabh Sharma was born at H.No. 70/11, Chougan, SunderNagar, Distt. Mandi (Himachal Pradesh) in India, on May 21, 1987. He
graduated from Dr. JJMCOE Jaysingpur, Distt. Kolhapur (Maharashtra), India, and completed his Masters of Technology(batch 200911), CSE Department, from Maharishi Markandeshwar Engg.
College, MMU, Mullana, Ambala(Haryana), INDIA. He has successfully completed his B.E. Final Year Project Multilayer Database Access Control Architecture(MDACA).