An Optimized CNN GNR Detector for Methane Gas Detection from HSI Raster Data Using Feature Variable
Keywords:
CNN Component filter, Gradients, Feature variable, Recursive filter, GNR DetectorAbstract
Methane gas is the Earth’s atmospheric second most significant greenhouse gas, so to reduce these emissions effective deep learning methods adopted. Methane source points and dimensions can be determined using airborne remote sensing AVIRIS-NG. The existing manual approaches, small pixel-footprint signal of the plumes causes them liable to human error and poorly scalable. The proposed Convolutional Neural Network (CNN) adapted from MATLAB toolbox to produce outcome of linear combinatorial pixel segmentation with accounting time. The target of the innovative is to segment the methane gas accurately considering minimization of the misclassification of plumes among terrain HSI raster data. Employing off-the-shelf CNN with available filters, the recursive filters applied to achieve long impulse response without having the reflectance to perform a long convolution. The SSIM metric evaluated the accuracy and the proposed approach produced outcome with the effectiveness of 98.21% precision, Recall of 96.89%, IOU of 93.93% and 98.36% F1-Score.
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