Endmember signature based detection of flammable gases in LWIR hyperspectral images

Omruuzun, Fatih; Çetin, Yasemin Yardımcı

doi:10.1117/12.2182060

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…These methods are applied to the resulting data cubes which are obtained after transforming the hyperspectral cubes into luminance temperature data. In this regard, in this study, the SAM-based method for gas detection was proposed by Öztürk et al [19] and the ACE-based gas detection method proposed by Omruuzun and Cetin [8] are selected as the baseline methods for the comparisons.…”

Section: A Experimental Results For Conventional Methodsmentioning

confidence: 99%

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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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Section: A Experimental Results For Conventional Methodsmentioning

confidence: 99%

“…The last step is the elimination of atmospheric effects from the data with the resulting black body curve. To this aim, one of the common methods, namely, the black-body radiation curve compensation algorithm [8] is adopted in this research. The final corrected data is calculated as,…”

Section: A Radiance To Luminance Temperature Conversionmentioning

confidence: 99%