Semi-supervised gas detection in hyperspectral imaging

Environmental pollution has recently aroused considerable public attention around the world. Air pollution is one form of pollution that is caused by the release of the chemical gases and particulates to the atmosphere. Remote monitoring of the chemical gas releasing facilities is urgently needed to regulate the amount of gas release levels. Hyperspectral imaging in long wave infrared (LWIR) spectrum is a popular way to monitor gas release in chemical facilities. In this study, we propose a novel approach towards semi-supervised detection/classification of gases in hyperspectral images. In our experiments, proposed approach is tested on 6 hyperspec-tral images and it achieves an average accuracy of 0.967.