RETRACTED ARTICLE: Edge detection of satellite image using fuzzy logic

Abstract

Edge detection plays an important role in the field of image processing. Various edge detection techniques are obtained like Sobel, PSO Preweitt, Laplacian and Laplacian of Gaussian. These techniques consume some restrictions such as fixed edge thickness and some parameter like threshold is problematic to implement. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. High performance computing (HPC) is a technique used to assemble process and analyze more quantity of remote sensing (satellite) data which needs less processing time. HPC method is used in distributed & cluster Computing, parallel computing and following satellite data analyzing & processing approaches like geo-referencing, image edge detection, image classification, image mosaicking, Morphological/neural approach and image fusion for hyperspectral satellite data. Stimulated by means of the specific clustering of theory called gravitational theory, an original edge detection process. The conservative methods include the usage of linear time invariant filters. Recognition of edge is done by these filters as a These filters recognize an edge as a sudden conversion of grey scale pixel intensity. These techniques are well recognized and computationally effective. Canny, Sobel, Robert, Kirsch, Prewitt and LOG, on idea of spatial differential filters by using local grade. Within the less time these filters process the data and are inclined to noise. But in fuzzy technique, it does not include like this restriction, by altering the rules and output parameters, it is possible to change the thickness of the edge. In this paper, a new technique is created on fuzzy logic, which is suggested for detection of edge in digital images deprived of finding the threshold value. This method starts through segmenting the images into sections by means of fluctuating 3 $$\times $$ 3 binary matrixes. The pixels of edge are planned to a range of standards separated from each other. The results of this proposed technique is compared with the results obtained from PSO and neural network methods. This suggested technique provides a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. And sharpness in the curves.