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

小故障

書目名稱Remote Sensing Intelligent Interpretation for Geology影響因子(影響力)




書目名稱Remote Sensing Intelligent Interpretation for Geology影響因子(影響力)學(xué)科排名




書目名稱Remote Sensing Intelligent Interpretation for Geology網(wǎng)絡(luò)公開度




書目名稱Remote Sensing Intelligent Interpretation for Geology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Remote Sensing Intelligent Interpretation for Geology被引頻次




書目名稱Remote Sensing Intelligent Interpretation for Geology被引頻次學(xué)科排名




書目名稱Remote Sensing Intelligent Interpretation for Geology年度引用




書目名稱Remote Sensing Intelligent Interpretation for Geology年度引用學(xué)科排名




書目名稱Remote Sensing Intelligent Interpretation for Geology讀者反饋




書目名稱Remote Sensing Intelligent Interpretation for Geology讀者反饋學(xué)科排名

旁觀者

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.

變白

魔鬼在游行

錯

flutter

978-981-99-8999-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

compose

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

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

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.

確保

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.