3D-CNN and autoencoder-based gas detection in hyperspectral images

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dc.authorid0000-0002-2829-3672en_US
dc.contributor.authorÖzdemir, Okan Bilge
dc.contributor.authorKoz, Alper
dc.date.accessioned2023-02-23T11:33:53Z
dc.date.available2023-02-23T11:33:53Z
dc.date.issued2023
dc.departmentAÇÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe 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 the thermal range neglect the fact that the captured radiance in the longwave infrared (LWIR) spectrum is better modeled as a mixture of the radiance of background and target gases, we propose a deep learning-based hyperspectral gas detection method in this article, which combines unmixing and classification. The proposed method first converts the radiance data to luminance-temperature data. Then, a 3-D convolutional neural network (CNN) 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 and adaptive cosine estimator is verified with LWIR hyperspectral images including methane and sulfur 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.
dc.identifier.citationÖzdemir, O. B., & Koz, A. (2023). 3D-CNN and Autoencoder-Based Gas Detection in Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1474–1482. ‌en_US
dc.identifier.doi10.1109/JSTARS.2023.3235781
dc.identifier.endpage1482en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage1474en_US
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2023.3235781
dc.identifier.urihttps://hdl.handle.net/11494/4719
dc.identifier.volume16en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÖzdemir, Okan Bilge
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral imagingen_US
dc.subjectGasesen_US
dc.subjectThree-dimensional displaysen_US
dc.subjectTemperature distributionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectSulfuren_US
dc.subjectNeural networksen_US
dc.title3D-CNN and autoencoder-based gas detection in hyperspectral imagesen_US
dc.typeArticle

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