DETECTING GAS IN HYPERSPECTRAL IMAGES USING 3DCNN AND AUTOENCODER

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 learningbased hyperspectral gas detection method in this article,
which combines unmixing and classification. The proposed method first converts the radiance data to luminancetemperature
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 And also it include an
ensemble model named CNN+BiGRU which got 100% accuracy for enhanced Autoencoder-Based Gas Detection
in Hyperspectral Images. A user-friendly Flask framework with SQLite integration facilitates signup and signin for
user testing, ensuring practical usability in deep learning applications.