Yazar "Yilmaz, Guray" seçeneğine göre listele

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    A new method for Anomaly Detection and Target Recognition
    (Ieee, 2014) Lokman, Gurcan; Yilmaz, Guray
    Use of unmanned Aerial Vehicles (UAVs) has gained significant importance in the recent years because they are capable of to be used in in civilian and military purposes for reconnaissance, surveillance, disaster relief, among other tasks. In this paper we present new automated anomaly detection and target recognition methodology that can be used on such a UAV. The standard paradigm for anomaly detection and target recognition in hyperspectral imagery (HSI) is to run a detection or recognition algorithm, typically statistical in nature, and visually inspect each high-scoring pixel to decide whether it is an anomaly or background data. A new method of anomaly detection and target recognition in HSI was studied based on a Neural Network (NN). Two multi-layered neural networks are used for anomaly detection and target recognition. The first phase of the model is used to detect anomalies in HSI. The second phase of the model is to use determine whether the anomaly is a predefined target or not. Both networks are trained in accordance with its intended purpose, so increase in performance is provided. This method can be a suitable solution for applications where the unmanned aerial vehicles used.
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    Öğe
    Anomaly Detection and Target Recognition With Hyperspectral Images
    (Ieee, 2014) Lokman, Gurcan; Yilmaz, Guray
    In this study, a new method that can perform anomaly detection and target recognition gradually with hyperspectral images (HSI) is introduced. This study constitutes the lower step a system that can process the HSI obtained by unmanned aerial vehicle (UAV) quickly and can enable the anomaly detection and target recognition process done in the mission time of UAV. In the proposed model, firstly the unusual pixels in the HSI data are detected, and then it is investigated whether these pixels are belonging to a target defined. These two phases in the model are carried out using artificial neural network that is a technique of the artificial intelligence. In the model, Parallel programming methods that run with CUDA platform on the GPU are proposed for the processing power requirement resulting from size of HSI.
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    Öğe
    Hyperspectral Image Classification Using Support Vector Neural Network Algorithm
    (Ieee, 2015) Lokman, Gurcan; Yilmaz, Guray
    With the developing technology, Hyperspectral images can be obtained with the satellites, aircraft and even unmanned aerial vehicles. Therefore, the classification applications made on the HSI are becoming increasingly important. In particular, fast and reliable classification algorithms are needed. The basic principle in classification algorithms is using characteristics of the data to find classification function that separate the data from each other. Neural Networks are among the non-linear classification method that can perform with high success. But, syntactic classifier has some problems that occur during training. One of this problems is called over-fitting. In many cases, especially in hyperspectral images, regularization is required for preventing the learning algorithm from over fitting the training data. In this study, a regularization scheme that named eigenvalue decay is used to make to this regularization in the training phase of networks. A training method that uses such a regularization scheme provides a margin maximization as in SVM for NNs. The two well-known data sets that are AVIRIS image of the Salinas Valley in California and image of Okavango Delta in Botswana acquired by The Hyperion sensor on NASA EO-1 satellite are used to test this classifier. The effectiveness of this algorithm on the HSI is evaluated using a series of experiments.
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    Öğe
    Target Detection in Hyperspectral Images Using Support Vector Neural Networks Algorithm
    (Ieee, 2015) Lokman, Gurcan; Yilmaz, Guray
    In this study, the use of Support Vector Neural Network (SVNN) algorithm is offered for target detection process in HSI. The basic principle in classification algorithms is using characteristics of the data to find classification function that separate the data from each other. Neural Networks are among the non-linear classification method that can perform with high success. The classification success depends on the training data and the training algorithm that are used. SVNN Algorithm is one of the methods used to increase the classification margin of the NNs. In this algorithm is provided a training method that used eigenvalue decay that provides a margin maximization as in SVM for NNs. In this context, a minimization problem that provide margin maximization for target detection in Hyperspectral images is defined and this problem is solved by Genetic Algorithms. In this way an algorithm that has high classification performance arises.