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Analysing of Hueckel Edge Detector Performance Using Satellite Image
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In this research, an analysis for the standard Hueckel edge detection algorithm behaviour by using three dimensional representations for the edge goodness criterion is presents after applying it on a real high texture satellite image, where the edge goodness criterion is analysis statistically. The Hueckel edge detection algorithm showed a forward exponential relationship between the execution time with the used disk radius. Hueckel restrictions that mentioned in his papers are adopted in this research. A discussion for the resultant edge shape and malformation is presented, since this is the first practical study of applying Hueckel edge detection algorithm on a real high texture image containing ramp edges (satellite image).

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Publication Date
Thu Jun 08 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Studying Hueckel Edge Detector Using Binary Step Edge Image
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Hueckel edge detector study using binary step edge image is presented. The standard algorithm that Hueckel presented, in his paper without any alteration is adopted. This paper studies a fully analysis for the algorithm efficiency, time consuming and the expected results with slide window size and edge direction. An analysis for its behavior with the changing of the slide window size (disk size) is presented. The best result is acquired when the window size equals to four pixel.  

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Publication Date
Sun Dec 03 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
Studying Hueckel edge detector using binary step edge image
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Practical Study for the Properties of Hueckel Edge Detection Operator
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Publication Date
Sun Feb 10 2019
Journal Name
Iraqi Journal Of Physics
A nonlinear edge –preserving smoothing filter for edge detection on color and gray satellite images
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A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.

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Publication Date
Mon Feb 04 2019
Journal Name
Iraqi Journal Of Physics
Satellite image classification using proposed singular value decomposition method
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In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that

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Publication Date
Wed May 31 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Satellite Image Mosaics Using Digital Cosine Transform (DCT) Descriptor
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In this research work, a modified DCT descriptor are presented to mosaics the satellite images based on Abdul Kareem [1] similarity criterion are presented, new method which is proposed to speed up the mosaics process is presented. The results of applying the modified DCT descriptor are compared with the mosaics method using RMSE similarity criterion which prove that the modified DCT descriptor to be fast and accurate mosaics method.

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Publication Date
Sun Aug 13 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Estimation Optimal Threshold Value for Image Edge Detection
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      A new approach presented in this study to determine the optimal edge detection threshold value. This approach is base on extracting small homogenous blocks from unequal mean targets. Then, from these blocks we generate small image with known edges (edges represent the lines between the contacted blocks). So, these simulated edges can be assumed as true edges .The true simulated edges, compared with the detected edges in the small generated image is done by using different thresholding values. The comparison based on computing mean square errors between the simulated edge image and the produced edge image from edge detector methods. The mean square error computed for the total edge image (Er), for edge regio

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Publication Date
Mon Oct 10 2016
Journal Name
Iraqi Journal Of Science
Satellite image classification using KL-transformation and modified vector quantization
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In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
COMPARATIVE STUDY FOR EDGE DETECTION OF NOISY IMAGE USING SOBEL AND LAPLACE OPERATORS
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Many approaches of different complexity already exist to edge detection in
color images. Nevertheless, the question remains of how different are the results
when employing computational costly techniques instead of simple ones. This
paper presents a comparative study on two approaches to color edge detection to
reduce noise in image. The approaches are based on the Sobel operator and the
Laplace operator. Furthermore, an efficient algorithm for implementing the two
operators is presented. The operators have been applied to real images. The results
are presented in this paper. It is shown that the quality of the results increases by
using second derivative operator (Laplace operator). And noise reduced in a good

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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