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Hybrid PCA–ResCNN–SVM for Molybdenite Grade Interval Recognition from VNIR–SWIR Spectra

https://doi.org/10.2139/ssrn.6643263
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.asr.2022.12.028openalex-snapshot
A deep residual convolutional neural network for mineral classification
inferredcites →https://doi.org/10.1016/j.gsf.2023.101562openalex-snapshot
Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data
inferredcites →https://doi.org/10.1080/01431161.2022.2133579openalex-snapshot
A comprehensive systematic review of deep learning methods for hyperspectral images classification
inferredcites →https://doi.org/10.1080/01431161.2023.2297178openalex-snapshot
Deep learning algorithms for hyperspectral remote sensing classifications: an applied review
inferredcites →https://doi.org/10.3390/min12080958openalex-snapshot
Advances on Exploration Indicators of Mineral VNIR-SWIR Spectroscopy and Chemistry: A Review
inferredcites →https://doi.org/10.3390/min13060754openalex-snapshot
Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data
inferredcites →https://doi.org/10.3390/mining4040057openalex-snapshot
VNIR-SWIR Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications
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