www.ijatir.org ISSN 2348–2370 Vol.07,Issue.05, June-2015, Pages:0568-0575 Copyright @ 2015 IJATIR. All rights reserved. A Novel Approach for Spectral Imagery Based on Edge Detector using Sparse Spatio-Spectral Masks G. JOHN BABU 1 , BORUSU RAMANI 2 , BOMMAKANTI VANI 3 1 Vice Principal, Vijaya Engineering College, India, E-mail: johnbabug@gmail.com. 2 PG Scholar, Vijaya Engineering College, India, E-mail: satyaraoborusu@gmail.com. 3 PG Scholar, Vijaya Engineering College, India, E-mail: bommakanti.vani@gmail.com. Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functional on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to- a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present by requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. We extend this simple detector using operators of several widths to cope with different signal-to- noise ratios in the image. Keywords: Edge Detection, Isoluminant Edge, Classification, Multicolor Edge Detection, Spatio-Spectral Mask, Spectral Ratios. I. INTRODUCTION Edge detectors of some kind, particularly step edge detectors, have been an essential part of many computer vision systems. The edge detection process serves to simplify the analysis of images by drastically reducing the amount of data to be processed, while at the same time preserving useful structural information about object boundaries. There is certainly a great deal of diversity in the applications of edge detection, but it is felt that many applications share a common set of requirements. These requirements yield an abstract edge detection problem, the solution of which can be applied in any of the original problem domains. We should mention some specific applications here. The Binford-Horn line finder used the output of an edge detector as input to a program which could isolate simple geometric solids. More recently the model-based vision system ACRONYM used an edge detector as the front end to a sophisticated recognition program. Shape from motion can be used to infer the structure of three-dimensional objects from the motion of edge contours or edge points in the image plane. Several modem theories of stereo sis assume that images are preprocessed by an edge detector before matching is done. Beattie describes an edge-based labeling scheme for low- level image understanding. Finally, some novel methods have been suggested for the extraction of three dimensional information from image contours, namely shape from contour and shape from texture. In all of these examples there are common criteria relevant to edge detector performance. The first and most obvious is low error rate. It is important that edges that occur in the image should not be missed and that there be no spurious responses. In all the above cases, system performance will be hampered by edge detector errors. The second criterion is that the edge points be well localized. That is, the distance between the points marked by the detector and the "center" of the true edge should be minimized. This is particularly true of stereo and shape from motion, where small disparities are measured between left and right images or between images produced at slightly different times. In this paper we will develop a mathematical form for these two criteria which can be used to design detectors for arbitrary edges. We will also discover that the first two criteria are not "tight" enough, and that it is necessary to add a third criterion to circumvent the possibility of multiple responses to a single edge. Using numerical optimization, we derive optimal operators for ridge and roof edges. We will then specialize the criteria for step edges and give a parametric closed form for the