Dr. Gülsen Taskin Kaya Profile

Dr. Gülsen Taskin Kaya

at Istanbul Technical Univ

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Publications (3)

Proceedings Article | 17 May 2016 Paper

Use of high dimensional model representation in dimensionality reduction: application to hyperspectral image classification

Gülşen Taşkin

Proceedings Volume 9840, 984017 (2016) https://doi.org/10.1117/12.2227326

KEYWORDS: Feature selection, Hyperspectral imaging, Image classification, Feature extraction, Silicon, Spectral resolution, Spatial resolution, Reliability, Earthquakes, Image analysis

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SPIE Journal Paper | 20 August 2015

Earthquake-induced damage classification from postearthquake satellite image using spectral and spatial features with support vector selection and adaptation

Gulsen Taskin, Okan Ersoy, Mustafa Kamasak

JARS, Vol. 9, Issue 01, 096017, (August 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096017

KEYWORDS: Image classification, Satellites, Earthquakes, Feature extraction, Satellite imaging, Earth observing sensors, Vegetation, Remote sensing, Image resolution, Roads

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Proceedings Article | 17 October 2013 Paper

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

Gülşen Taşkin Kaya

Proceedings Volume 8892, 88920G (2013) https://doi.org/10.1117/12.2030100

KEYWORDS: Near infrared, Feature selection, Earthquakes, Distributed interactive simulations, Satellites, Earth observing sensors, High resolution satellite images, Remote sensing, Satellite imaging, Image processing

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Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

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