Rock-Ring Accuracy Improvement in Infrared Satellite Image with Subpixel Edge Detection

Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.

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FPGA based Edge Detection using Sobel Filter

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36276 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 145-147

Author(s):

Gabbar Jadhav

Keyword(s):

Image Processing ◽

Edge Detection ◽

Noise Suppression ◽

Satellite Image ◽

Differentiation Operator ◽

Considerable Extent ◽

Minimal Amount ◽

Gradient Vector ◽

Sobel Operator ◽

Correct Form

In image processing, Sobel operator is utilised especially inside algorithms of edge-detection. It is a discreet differentiation operator which calculates the gradient approximation of the function picture intensity. The outcome of the Sobel operation at each location of the image is either the appropriate gradient vector or the vector standard. The Sobel operator relies on the image being converted into horizontal and vertical with a tiny, separable and integrated valued filter. This means that the computation is quite inexpensive. PAN Poanta satellite image was used for this work using Java, Core Java in GDAL package. As compared to in built Sobel operator, the image generated for this work is very fine and sharp as a result of noise suppression to a considerable extent. Inorder to do edge detection efficiently with minimal amount of false results, a correct form of Sobel filter ( I’=√(I*X)²+(I*Y)2 ) was used instead of the approximation(I’=I*X+I*Y) for the sake of computation.

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Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2020040105 ◽

2020 ◽

Vol 11 (2) ◽

pp. 80-94

Author(s):

Tapan Sharma ◽

Vinod Shokeen ◽

Sunil Mathur

Keyword(s):

Edge Detection ◽

Artificial Bee Colony ◽

Satellite Image ◽

Detection Algorithms ◽

Distributed Approach ◽

Bee Colony ◽

Image Edge Detection ◽

Nature Inspired Algorithm ◽

Canny Edge ◽

Image Edge

The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

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