Başkurt, Nur Didem
Gur, Yusuf
Omruuzun, Fatih
Çetin, Yasemin
Hyperspectral imaging for gas detection applications is an under-researched topic. The same gas model is used in most of the gas detection studies in the literature. This model aims to formulate the scene covering the gas emission as well as the background and the atmosphere. Therefore, the model requires prior knowledge on transmittance, emissivity, and temperature values of the components in the scene. The commonly used approaches to estimate these parameters include atmospheric modeling and statistical inference. However, accessing such information is costly in remote detection applications. Some studies avoid background characterization by decomposing the scene using spectral-spatial information.


Absorbance Estimation and Gas Emissions Detection in Hyperspectral Imagery
Başkurt, Nur Didem; Gur, Yusuf; Omruuzun, Fatih; Çetin, Yasemin (2016-05-19)
Hyperspectral imaging in gas detection applications is a leading and widely studied research topic thanks to its high spectral resolution and remote detection ability. The main problems in these applications covers the leakage detection, gas identification, and quantification. The proposed algorithm aims to reach the transmittance and absorbance features of the gas and to detect the gaseous region by using the measured radiance data from the hyperspectral infrared sensors.
Gas Detection in Longwave Infrared Hyperspectral Imagery and Black Body Effect Compensation
Omruuzun, Fatih; Çetin, Yasemin (2015-05-19)
Hyperspectral imaging, as a relatively new remote sensing method, provides dense information regarding the energy absorption and reflection characteristics of materials located in the scene being investigated. Therefore, material detection and identification performance in these images increases as compared to conventional remote sensing methods. Hyperspectral sensors that are operating in infrared regions enables detection of materials in gaseous form. This study proposes a method for improving gas detecti...
Computational Spectral Imaging with Photon Sieves
Öktem, Sevinç Figen; Davila, Joseph M. (2016-01-01)
Spectral imaging, the sensing of spatial information as a function of wavelength, is a widely used diagnostic technique in diverse fields such as physics, chemistry, biology, medicine, astronomy, and remote sensing. In this paper, we present a novel computational imaging modality that enables high-resolution spectral imaging by distributing the imaging task between a photon sieve system and a computer. The photon sieve system, coupled with a moving detector, provides measurements from multiple planes. Then ...
Omruuzun, Fatih; Baskurt, Didem Ozisik; Çetin, Yasemin (2015-06-05)
Remote sensing and detection of gases using hyperspectral imagery is an emerging research topic. Recent developments in infrared sensor technology increase usage of these devices in various fields ranging from environmental applications to defence industry. Another research topic in remote sensing field is the detection and identification of gas emissions using these devices. This study proposes a method for improving detection accuracy of CO2 emissions in Mid-Wave Infrared (MWIR) hyperspectral imagery with...
Transmission and detection of terahertz radiation in a weakly ionized plasma (Conference Presentation)
TAKAN, TAYLAN; Alasgarzade, Namıg; YILDIRIM, İHSAN OZAN; Uzun Kaymak, İlker Ümit; ŞAHİN, ASAF BEHZAT; Altan, Hakan (2017-01-27)
Plasma, used as a terahertz (THz) detection medium has promising features. Several studies for mm-wave/THz radiation detection using various kind of methods for plasma creation such as neon indicator lamps [1], gas cells [2] and laser-induced air plasma [3] have been conducted. The interaction between the plasma and various frequency EM waves are still being investigated and in the mm-wave/terahertz range the interaction mechanism is still not well understood. In this study a home-built gas chamber with var...
Citation Formats
N. D. Başkurt, Y. Gur, F. Omruuzun, and Y. Çetin, “GAS DETECTION BY USING TRANSMITTANCE ESTIMATION AND SEGMENTATION APPROACHES,” 2016, vol. 10008, Accessed: 00, 2020. [Online]. Available: