Neural Network Change Detection Model for Satellite Images Using Textural and Spectral Characteristics

Helmy, Ashraf K.; El-Taweel, Gh. S.

doi:10.3844/ajeassp.2010.604.610

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…It is most useful for feed-forward networks (networks that have no feedback, or simply, that have no connections that loop). Helmy and El-Taweel (2010) developed a neural network model for satellite images based on both spectral and textural analysis. Better discrimination with 23% higher accuracy was achieved in the trained network with textural features than without textural parameters.…”

Section: Classificationmentioning

confidence: 99%

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Karthikeyan¹

2014

American Journal of Applied Sciences

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Glaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the preprocessed image as textural features reflects physiological changes in the fundus images. Second order textural features are extracted for different quantization levels namely 8, 16, 32, 64, 128 and 256 in four orientations viz 0, 45, 90 and 135° for various distances. Extracted features are selected using Sequential Forward Floating Selection (SFFS) technique.The selected features are fed to Back Propagation Network (BPN) for classification as normal and abnormal images. The proposed computer aided diagnostic system achieved 96% sensitivity, 94% specificity, 95% accuracy and can be used for screening purposes. In this study, the analysis of gray levels have shown their significance in the classification of glaucoma.

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Section: Classificationmentioning

confidence: 99%

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Karthikeyan¹

2014

American Journal of Applied Sciences

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“…Texture feature extraction has been widely done using Gabor filters (Greenspan et al, 2007). Texture features are compared based on the dissimilarity between vector features using Euclidean of Mahalanobis distance (Helmy et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network

Durai¹

2011

Journal of Computer Science

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Problem statement: With extensive digitization of images, diagrams and paintings, traditional keyword based search has been found to be inefficient for retrieval of the required data. Content-Based Image Retrieval (CBIR) system responds to image queries as input and relies on image content, using techniques from computer vision and image processing to interpret and understand it, while using techniques from information retrieval and databases to rapidly locate and retrieve images suiting an input query. CBIR finds extensive applications in the field of medicine as it assists a doctor to make better decisions by referring the CBIR system and gain confidence. Approach: Various methods have been proposed for CBIR using image low level image features like histogram, color layout, texture and analysis of the image in the frequency domain. Similarly various classification algorithms like Naïve Bayes classifier, Support Vector Machine, Decision tree induction algorithms and Neural Network based classifiers have been studied extensively. We proposed to extract features from an image using Discrete Cosine Transform, extract relevant features using information gain and Gaussian Fuzzy Feed Forward Neural Network algorithm for classification. Results and Conclusion: We apply our proposed procedure to 180 brain MRI images of which 72 images were used for testing and the remaining for training. The classification accuracy obtained was 95.83% for a three class problem. This research focused on a narrow search, where further investigation is needed to evaluate larger classes.

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“…The program has produced many advances in stealth technology and in designing and manufacturing methods. These achievements have been applied to other Boeing programs, including the F/A- Abbas, 2009;Abu-Ein, 2009;Opafunso et al, 2009;Semin et al, 2009a-c;Zulkifli et al, 2009;Ab-Rahman et al, 2009;Abdullah and Halim, 2009;Zotos and Costopoulos, 2009;Feraga et al, 2009;Bakar et al, 2009;Cardu et al, 2009;Bolonkin, 2009a-b;Nandhakumar et al, 2009;Odeh et al, 2009;Lubis et al, 2009;Fathallah and Bakar, 2009;Marghany and Hashim, 2009;Kwon et al, 2010;Aly and Abuelnasr, 2010;Farahani et al, 2010;Ahmed et al, 2010;Kunanoppadon, 2010;Helmy and El-Taweel, 2010;Qutbodin, 2010;Pattanasethanon, 2010;Fen et al, 2011;Thongwan et al, 2011;Theansuwan and Triratanasirichai, 2011;Smadi, 2011;Tourab et al, 2011;Raptis et al, 2011;Momani et al, 2011;Ismail et al, 2011;Anizan et al, 2011;Tsolakis and Raptis, 2011;Abdullah et al, 2011;Kechiche et al, 2011;Ho et al, 2011;…”

Section: Introductionmentioning

confidence: 99%

About Boeing X-32

Petrescu¹

2019

Journal of Aircraft and Spacecraft Technology

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The Boeing X-32 is a demonstration jet designed for the Joint Strike Fighter contest. He lost the Lockheed Martin X-35 demonstrator, which was further developed in the Lockheed Martin F-35 Lightning II. Boeing's competitive advantage has been to provide considerably lower costs of production and lifecycle by minimizing variations between different versions of the JSF. Therefore, the X-32 was designed around a delta wing of the carbon fiber composite. The wing had an opening of 9.15 meters, with a current of 55 degrees on the front edge and could have up to 20,000 kilograms of fuel. The purpose of the large inclination angle was to allow the use of a thick wings section while obtaining limited transonic aerodynamic resistances and a good angle for the corresponding antenna equipment installed in the wing. Wings would be a challenge to be done. The cost competition strategy also led Boeing to choose a direct traction vectoring system for the short-term STOVL landing requirement, as this would require the addition of a traction vectoring module around the main engine. However, this choice requires the engine to be mounted directly behind the cockpit and changing the center of gravity before being ordinarily positioned in the jets of the plane (behind the plane) to allow neutral movement of the attitude. Boeing proposed in the 1960s a supersonic fighter as a gravity motor with vector pushing nozzles, but never surpassed the images published in the aviation week. By comparison, Lockheed's entry showed, if not, a smaller version of the F-22 Raptor stealth fighter. The nickname of X-32, Boeing, was "Monica. " However, another direct lifting selection effect was the chrome-air intake, similar to the Vought F-8 Crusader and LTV A-7 Corsair II. This was necessary to provide sufficient air for the main engine (to provide the force of support) during the zero speed horizon when it could not exploit the ram air pressure. A blow to the effect of this large input was the direct potential visibility of the compressor blades to the radar. Among the possibilities of attenuation were the variable moments designed to lock the received radio waves without adversely affecting the air flow.

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Neural Network Change Detection Model for Satellite Images Using Textural and Spectral Characteristics

Cited by 22 publications

References 17 publications

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network

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