Browsing by Author "Ozturk, Safak"

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Citation Count: 3
Comparison of Target Detection Performance for Radiance and Reflectance Domain in Vnir Hyperspectral Images
(Ieee, 2019) Ozdil, Omer; Güneş, Ahmet; Gunes, Ahmet; Esin, Yunus Emre; Demirel, Berkan; Ozturk, Safak; Department of Mechatronics Engineering
In this paper, the hyperspectral detection of targets in visible-near infrared (VNIR) images is studied. The change of radiance domain signatures in images taken in different locations, time and altitudes are analyzed. A new radiance domain detection scheme for VNIR images under 1000 m altitude is proposed. The analysis shows that the radiance domain signatures of each target, that are collected from an image taken at 10 m altitude, can be effectively used for pure pixel target detection in other VNIR images taken at altitudes between 10 - 1000 m. The proposed approach is tested using several target types and on images taken at different altitudes and environmental conditions. Our results show that target detection in radiance domain provides a cheaper, easier and effective alternative to reflectance domain, in VNIR images.
Citation Count: 0
4-stage< Target Detection Approach in Hyperspectral Images
(Ieee, 2018) Ozdil, Omer; Güneş, Ahmet; Gunes, Ahmet; Esin, Yunus Emre; Ozturk, Safak; Demirel, Berkan; Department of Mechatronics Engineering
Practical target detection systems require an automatic way to detect targets with high accuracy. Detection errors is not tolerable and they should be reduced as much as possible. In classical detection systems, generally single target detection algorithm is performed and the result will be evaluated according to the thresholding techniques. However, in these uncontrolled systems, false alarm rate strongly depends on the thresholding technique success. It is very hard to find a general and constant threshold value for images taken at different conditions and practical detection systems needs reliable threshold value. In this paper, we propose a new multi-stage target detection system which is the combination of different detection algorithms and thresholding technique. This system compose of 4-stages, i.e. namely 1-initial target detection (ACE, GLRT), 2-adaptive Constant False Alarm Rate (CFAR) thresholding, 3-spatially grouping, 4-statistical confidence operation. This system configuration removes the need for interactive user and it automatically implements confirmation and rejection steps. Moreover, this system can be used both for pure pixel and subpixel target detection purposes and it reduces computational processing time considerably with the implementation of consequtive processing stages.