小故障
发表于 2025-3-21 16:58:01
书目名称Remote Sensing Intelligent Interpretation for Geology影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0826897<br><br> <br><br>书目名称Remote Sensing Intelligent Interpretation for Geology读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0826897<br><br> <br><br>
旁观者
发表于 2025-3-21 22:29:21
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.
变白
发表于 2025-3-22 02:27:31
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魔鬼在游行
发表于 2025-3-22 04:45:01
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错
发表于 2025-3-22 12:43:51
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flutter
发表于 2025-3-22 12:56:41
978-981-99-8999-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
compose
发表于 2025-3-22 19:02:26
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),
aerial
发表于 2025-3-22 21:45:28
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.
Enervate
发表于 2025-3-23 03:07:35
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.
确保
发表于 2025-3-23 08:54:17
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.