EDGE DETECTION ENHANCED TECHNIQUE OF SATELLITE IMAGES USING DESIGN DIGITAL FILTER

Most image’s interpreters are concerned with recognizing linear features in images. For geological purposes, faults, joints, features, dykes and geological contacts are the main linear features which must be interpreted. The linear features are formed by edges, where some edges are marked by pronounced differences in brightness and become ready recognized. This paper deals with satellite image edge detection techniques. Edge detection is the name for a set of mathematical methods which aim at identifying points in a satellite image at which the image brightness changes sharply or, more formally, has discontinuities. These points are typically organized into a set of curved line segments termed edges. Important features can be extracted from the edges of an image (e.g., corners, geological boundary, linesand curves). In this paper, a novel edge detection technique which computes edges of satellite image using two dimensional (2D) design digital filter is presented and applied on Gabal Gattar granitic batholith to enhance boundaries between granite and surrounded rocks in one hand, and between the granitic phases on another hand, as well as, enhance the shape of granitic masses, and faults, dykes, fractures cutting them. Here the proposed methodology is compared with other edge detectors. Filters (Sobel, Roberts, Prewitt, Canny and Log filters) as applied on G. Gattar granitic mass. This study reveals that the new proposed (Raafat filter) porttery the contacts and linear features more obvious than the other operators. INRTODUCTION Edge detection is a fundamental tool in image processing, image analysis and image pattern recognition, particularly in the areas of feature detection and feature extraction. Generally, due to the variation of image features such as brightness, edges can be recognized. Mainly the edges are indication of the discontinuities of image intensity function. In image processing, edges give valuable and very important information towards human image understanding. Edge detection has become very serious challenge to the scientist as well as researchers related to image processing. Contrast enhancement many emphasize brightness difference associated with some features, but this procedure is not specific for linear features because elements of the scene are enhanced equally, not just the linear elements. Edge enhancement image attempt to presence both local contrast and low frequency brightness information. They are produces by adding back all or a portion of the gray levels in the original image to a high frequency component image of the same scene (Lillsand and Kiefer, 2000). The main objective of edge detection is to indicate the points in a digital image where the luminous intensity changes very sharply. Due to sharp changes in image properties, it reflects the valuable events and changes in several properties. For discontinuities, there could be a ISSN 2314-5609 Nuclear Sciences Scientific Journal 7, 205-213 2018 http://www.ssnma. com 206 RAAFAT M. MOHAMED and AHMED I. KAMEL number of reasons, such as lighting conditions, type of material surface, texture, object geometry etc., and their mutual interaction. Edge detection methodology is a process of detection of this type of discontinues in an image. Generally, the implementation of derivative operator on intensity image generates another image, which is called as gradient image due to its rate of intensity variation. The major structural properties can be represented in a transparent manner of an image due to the edge detection mechanism. Many methods can be implemented for edge detection. There are many traditional operators such as Sobel operator, Prewitt operator, Laplacian of Gaussian operator, Canny operator and Robert operator which are used for edge detection of images. METHODOLGY Edge enhancement is implemented, firstly, produced a high frequency component image containing the edge information followed by added back all or a fractures of gray level in each pixel of the original scene to the high frequency component image. The final image containing local contrast enhancement of high frequency features (Lillsand and Kiefer, 2000). Some linear features occurred as narrow linear against a background, while linear contrast is located between adjacent areas of different brightness in images. These linear features are formed by edges. In images, some edges are easily recognized due to their pronounced differences in brightness, while other which marked by subtle brightness differences are difficult to recognize. Digital filters have been developed specifically to enhance edges in image. These filters fall into two categories, directional and non-directional filters (Sabin, 1996). Directional filters aimed at emphasizing edges in image data. It is procedure that systematically compare each pixel in an image to one of its immediately adjacent neighbors and displays the difference in terms of gray levels of an output image. This process is mathematically akin to determining the first derivative of gray levels with respect to a given direction (horizontal, vertical and diagonal) (Lillsand and Kiefer, 2000). In order to design the 2D digital filter, we used a second order polynomial as follows: Second Order Polynomial Suppose that, we have 5x5 data points and wish to fit the observed data, in some sense, by the following polynomial: