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Titlebook: Remote Sensing Intelligent Interpretation for Geology; From Perspective of Weitao Chen,Xianju Li,Lizhe Wang Book 2024 The Editor(s) (if ap

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发表于 2025-3-21 16:58:01 | 显示全部楼层 |阅读模式
书目名称Remote Sensing Intelligent Interpretation for Geology
副标题From Perspective of
编辑Weitao Chen,Xianju Li,Lizhe Wang
视频videohttp://file.papertrans.cn/827/826897/826897.mp4
概述Presents interpretable intelligence interpretation theory on remote sensing geology.Constructs geological remote sensing datasets from multi-level as a basis for intelligent interpretation.Presents no
图书封面Titlebook: Remote Sensing Intelligent Interpretation for Geology; From Perspective of  Weitao Chen,Xianju Li,Lizhe Wang Book 2024 The Editor(s) (if ap
描述.This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance.  ..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.  ..The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration..
出版日期Book 2024
关键词Geological exploration; multimodal remote sensing; Geology intelligent interpretation; Interpretable de
版次1
doihttps://doi.org/10.1007/978-981-99-8997-3
isbn_softcover978-981-99-8999-7
isbn_ebook978-981-99-8997-3
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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发表于 2025-3-21 22:29:21 | 显示全部楼层
Geological Remote Sensing Dataset Construction for Multi-level Tasks,This chapter introduce the lithology datasets preparing for the intelligent interpretation methods in the following chapters. For each dataset, the basic situation of the study area, remote sensing data sources, the preprocessing approaches and overview of datasets are introduced.
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978-981-99-8999-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
发表于 2025-3-22 19:02:26 | 显示全部楼层
Book 2024esearch subjects of this book include lithology and mineral abundance.  ..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),
发表于 2025-3-22 21:45:28 | 显示全部楼层
Multi-view Lithology Remote Sensing Scene Classification Based on Transfer Learning, scene classification model based on multi-view data fusion, and proposes a transfer learning method based on multi-view data fusion, which can achieve the identification of new lithology types across regions and improve the model generalization ability.
发表于 2025-3-23 03:07:35 | 显示全部楼层
Hyperspectral Remote Sensing Inversion of Mineral Abundance Based on Sparse Unmixing Method, by introducing the superpixel segmentation algorithm. Taking the Cuprite dataset as an example, which is a real mining dataset, experiments indicate that the sparse unmixing algorithm achieves satisfactory results on this dataset.
发表于 2025-3-23 08:54:17 | 显示全部楼层
Lithological Scene Classification Based on Model Migration and Fine-Tuning Strategy,rce domain, and also improved the classification accuracy with only limited samples. OA and F1_score, and Kappa on the normal test set were 61.52 ± 0.95%, 55.58 ± 2.58%, and 52.18 ± 1.01%, respectively, and on a small sample test set were 47.40 ± 0.65%, 49.58 ± 0.41%, and 40.41 ± 0.45%, respectively, which were superior to the direct training.
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