Ms. Hilal Soydan Profile

Hilal Soydan

Research Assistant at Middle East Technical Univ

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Proceedings Article | 1 September 2017 Paper

Spatio-temporal anomaly detection for environmental impact assessment: a case of an abandoned coal mine site in Turkey

Hilal Soydan, Alper Koz, H. Şebnem Düzgün

Proc. SPIE. 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV

KEYWORDS: Landsat, Detection and tracking algorithms, Image analysis, Mining, Environmental sensing

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The main purpose of this research is to determine the anomalies regarding with the coal mining operations in an abandoned coal mine site in central Anatolia by multi-temporal image analysis of Landsat 4-5 surface reflectance data. A well-known anomaly detection algorithm, Reed-Xioli (RX), which calculates square of Mahalanobis metrics to calculate the likelihood ratios by normalizing the difference between the test pixel and the background to allocate anomaly pixels, is implemented across the time series. The experimental results reveal especially the profound land use – land cover change in time series, pointing out critically abandoned regions that need immediate rehabilitation action. The rate of anomaly scores together with their relation to mine development over the focused time spectrum discloses a linearity trend as of the operations are ceased at the end of 1990s, which is indicative of the capacity of the applied method. The performance of the algorithm is also quantified with Receiver Operating Characteristics (ROC) curves and precisionrecall graphs to quantify its capability on Landsat Thematic Mapper (TM) multispectral image series. The resulting plots show the increasing capability of the hyperspectral anomaly detection technique in multi-temporal data set, with a steady and slight increase in performance between 2000 and 2012 after the end of the mining activities, which substantiates the success of global RX algorithm to identify the mining-induced land use and land cover anomalies.

Proceedings Article | 20 October 2015 Paper

Hydrocarbon microseepage mapping using signature based target detection techniques

Hilal Soydan, Alper Koz, H. Şebnem Düzgün, A. Aydin Alatan

Proc. SPIE. 9644, Earth Resources and Environmental Remote Sensing/GIS Applications VI

KEYWORDS: Target detection, Optical filters, Visible radiation, Minerals, Detection and tracking algorithms, Remote sensing, Single mode fibers, Carbonates, Atmospheric corrections, Absorption

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In this paper, we compare the conventional methods in hydrocarbon seepage anomalies with the signature based detection algorithms. The Crosta technique [1] is selected as a basement in the experimental comparisons for the conventional approach. The Crosta technique utilizes the characteristic bands of the searched target for principal component transformation in order to determine the components characterizing the target in interest. Desired Target Detection and Classification Algorithm (DTDCA), Spectral Matched Filter (SMF), and Normalized Correlation (NC) are employed for signature based target detection. Signature based target detection algorithms are applied to the whole spectrum benefiting from the information stored in all spectral bands. The selected methods are applied to a multispectral Advanced SpaceBorne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction prior to the realization of the algorithms. ASTER provides multispectral bands covering visible, short wave, and thermal infrared region, which serves as a useful tool for the interpretation of the areas with hydrocarbon anomalies. The exploration area is selected as Gemrik Anticline which is located in South East Anatolia, Adıyaman, Bozova Oil Field, where microseeps can be observed with almost no vegetation cover. The spectral signatures collected with Analytical Spectral Devices Inc. (ASD) spectrometer from the reference valley [2] have been utilized as an input to the signature based detection algorithms. The experiments have indicated that DTDCA and MF outperforms the Crosta technique by locating the microseepage patterns along the mitigation pathways with a better contrast. On the other hand, NC has not been able to map the searched target with a visible distinction. It is concluded that the signature based algorithms can be more effective than the conventional methods for the detection of microseepage induced anomalies.

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