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Total Size:
9.6 MB
Info Hash:
F9A2EAE46E71876D72A6DFB1BE90C4EEF92852F7
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Added:
April 20, 2026, 6:01 a.m.
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(Last updated: April 20, 2026, 6:01 a.m.)
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| ['Chen W. Remote Sensing Intelligent Interpretation for Geology...2024.pdf'] | 0 bytes |
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NOTE
SOURCE: Chen W. Remote Sensing Intelligent Interpretation for Geology...2024
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COVER
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MEDIAINFO
Textbook in PDF format This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing
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