Search Results for author:
Found 3 papers, 2 papers with code
Remote sensing framework for geological mapping via stacked autoencoders and clustering
In this study, we present an unsupervised machine learning framework for processing remote sensing data by utilizing stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units.
A review of machine learning in processing remote sensing data for mineral exploration
As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits.
Computer vision-based framework for extracting geological lineaments from optical remote sensing data
We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
