Detection and identification of effluent gases by long wave infrared (LWIR) hyperspectral images

Sagiv, Lior; Rotman, Stanley R.; Blumberg, Dan G.

doi:10.1109/eeei.2008.4736560

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…From Eq. (12) and Eq. (23), we know that GEBTD relates to the background surface temperature b T and emissivity b ε , gas absorption coefficient p α , concentration p c , and the path length l. In our experiments, the background surface temperature b T is 20℃ and the emissivity b ε is 0.967.…”

Section: Detectivity Analysismentioning

confidence: 97%

Detectivity of gas leakage based on electromagnetic radiation transfer

Long

Wang

et al. 2011

SPIE Proceedings

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Standoff detection of gas leakage is a fundamental need in petrochemical and power industries. The passive gas imaging system using thermal imager has been proven to be efficient to visualize leaking gas which is not visible to the naked eye. The detection probability of gas leakage is the basis for designing a gas imaging system. Supposing the performance parameters of the thermal imager are known, the detectivity based on electromagnetic radiation transfer model to image gas leakage is analyzed. This model takes into consideration a physical analysis of the gas plume spread in the atmosphere-the interaction processes between the gas and its surrounding environment, the temperature of the gas and the background, the background surface emissivity, and also gas concentration, etc. Under a certain environmental conditions, through calculating the radiation reaching to the detector from the camera's optical field of view, we obtain an entity "Gas Equivalent Blackbody Temperature Difference (GEBTD)" which is the radiation difference between the on-plume and off-plume regions. Comparing the GEBTD with the Noise Equivalent Temperature Difference (NETD) of the thermal imager, we can know whether the system can image the gas leakage. At last, an example of detecting CO 2 gas by JADE MWIR thermal imager with a narrow band-pass filter is presented.

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Section: Detectivity Analysismentioning

confidence: 97%

Detectivity of gas leakage based on electromagnetic radiation transfer

Long

Wang

et al. 2011

SPIE Proceedings

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“…In the literature, most of the studies use luminance temperature of hyperspectral data for gas detection applications [6]- [9], [11], [14], [15], [17], [19] as such information indicates a more steady characteristic than the radiance data for varying conditions. The conversion of hyperspectral radiance data to luminance temperatures is carried out in three stages.…”

Section: A Radiance To Luminance Temperature Conversionmentioning

confidence: 99%

“…In another study, Spisz et al [13] first applied principal component analysis for background removal and then utilize matched filter and spectral angle mapper to detect various chemical compounds. A different study using hyperspectral imaging (HSI) [14] focused on the automatic detection of waste gases. The proposed method first filters the possible areas in the scene by means of detecting critical wavelengths and using the correlation coefficient metrics to select pixels with high concentration.…”

Section: Introductionmentioning

confidence: 99%

3D-CNN and Autoencoder-Based Gas Detection in Hyperspectral Images

Özdemir

Koz

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The detection of gas emission levels is a crucial problem for ecology and human health. Hyperspectral image analysis offers many advantages over traditional gas detection systems with its detection capability from safe distances. Observing that the existing hyperspectral gas detection methods in thermal range neglect the fact that the captured radiance in longwave infrared (LWIR) spectrum is better modelled as a mixture of the radiance of background and target gases, we propose a deep learning based hyperspectral gas detection method in this paper, which combines unmixing and classification. The proposed method first converts the radiance data to luminance-temperature data. Then, a 3D convolutional neural network and autoencoder-based network, which is specially designed for unmixing, is applied to the resulting data to acquire abundances and endmembers for each pixel. Finally, the detection is achieved by a three-layer fully connected network to detect the target gases at each pixel based on the extracted endmember spectra and abundance values. The superior performance of the proposed method with respect to the conventional hyperspectral gas detection methods using spectral angle mapper (SAM) and adaptive cosine estimator (ACE) is verified with LWIR hyperspectral images including Methane and Sulphur Dioxide gases. In addition, the ablation study with respect to different combinations of the proposed structure including direct classification and unmixing methods has revealed the contribution of the proposed system.

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“…The Telops Hyper-Cam sensor has been used in several ground-based field campaigns, including the demonstration of standoff chemical agent detection [1,2]. Other applications such as atmospheric science, surface contaminant detection, soil characterization and combustion analysis are described in this paper.…”

Section: Introductionmentioning

confidence: 99%

Organizing committee

2010

2010 20th International Crimean Conference "Microwave &Amp; Telecommunication Technology"

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Modern defence acitivites often require sophisticated technology to enable protection of sites and troops, as well as understanding and predicting events in the battlefield. The need for protection against chemical attacks has been a military priority for decades. Similarly, the need for battlefield standoff remote sensing (target/camouflage and surface contaminants detection, IR signatures of decoys/flares/jet engines) has evolved along with theelectrooptical technologies. These areas of application require powerful detection methods through the analysis of infrared spectral signatures and recognition algorithms.Telops commercializes the Hyper-Cam, a rugged and compact infrared hyperspectral imaging sensor operating in the LWIR (8-11.5 ȝm) or the MWIR (1.5-5.5 ȝm). This paper presents some areas of application in defence innovative research where the Hyper-Cam sensor, is paving the way with unprecedented capabilities.

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Detection and identification of effluent gases by long wave infrared (LWIR) hyperspectral images

Cited by 6 publications

References 12 publications

Detectivity of gas leakage based on electromagnetic radiation transfer

Detectivity of gas leakage based on electromagnetic radiation transfer

3D-CNN and Autoencoder-Based Gas Detection in Hyperspectral Images

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