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

Abstract

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 conventionalhyperspectral 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.