3D-CNN and autoencoder-based gas detection in hyperspectral images
Yükleniyor...
Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 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.
Açıklama
Anahtar Kelimeler
Hyperspectral imaging, Gases, Three-dimensional displays, Temperature distribution, Convolutional neural networks, Sulfur, Neural networks
Kaynak
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
16
Sayı
Künye
Özdemir, 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.
