An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing

Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performanc...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2024-10, Vol.181, p.106178, Article 106178
Main Authors: Bansal, Kamakhya, Tripathi, Ashish Kumar
Format: Article
Language:English
Subjects:
carbon dioxide
computer software
Convolutional neural networks (CNN)
cost effectiveness
Earth observation imagery
Grad-CAM
greenhouse gases
methane
Methane source classification
remote sensing
Scaled dot product attention (SDPA)
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