A Framework for Land Use Scenes Classification Based on Landscape Photos
文献类型：期刊论文
| 作者 | Xu, Shiwu; Zhang, Shihui; Zeng, Jue4,5; Li, Tingyu; Guo, Qinghua1,6 ; Jin, Shichao1,2,6
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2020 |
| 卷号 | 13页码:6124-6141 |
| 关键词 | Semantics Remote sensing Image analysis Image segmentation Object recognition Machine learning Forestry Deep convolutional neural networks (DCNNs) landscape photos land survey land use scene classification |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2020.3028158 |
| 文献子类 | Article |
| 英文摘要 | Space-earth integrated stereoscopic mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining deep learning and remote sensing images for the classification of land use scenes. However, improvement of classification accuracy has been limited because of the lack of sectional representation. Landscape photos can describe the cross-sections in detail. For this reason, this study constructed a land-use semantic photo dataset (LSPD) and proposed a land-use classification framework for photos (LUCFP) based on Inception-v4. LSPD was constructed through semantic planning, scene segmentation, supervised iteration transfer learning, and augmentation of photos. LSPD has 1.4 million photos collected from seven geographic regions of China, and covers 13 land-use categories and 44 semantic categories. LUCFP adapts scene segmentation based on depth of field, multisemantic block labeling, and weighting of semantic joint spatial ranges to determine the land use category. To validate LUCFP, nine semantic samples (9x3x2000 photos) were chosen from LSPD, obtaining an overall accuracy of 97.64%. The best photo cropping method was masking, which crops the boundary of the scene labeled by the photo, leading to an accuracy of 90.32%. The optimal pixel size that balances speed and accuracy is 675x675, with speed reaching 30 photos per second with an average accuracy of 93.80%. LUCFP has been successfully applied to the automatic verification of land surveys in China. |
| 学科主题 | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | PISCATAWAY |
| 电子版国际标准刊号 | 2151-1535 |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; SELECTION ; GRADIENT |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000579341600009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | China Institute of Land Surveying and Planning [2019114017] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/21554]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Agr Univ, Plant Phen Res Ctr, Nanjing 210095, Jiangsu, Peoples R China 4.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Hubei, Peoples R China 5.China Univ Geosci, Wuhan 430074, Hubei, Peoples R China 6.China Inst Land Surveying & Planning, Beijing 100035, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Shiwu,Zhang, Shihui,Zeng, Jue,et al. A Framework for Land Use Scenes Classification Based on Landscape Photos[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2020,13:6124-6141. |
| APA | Xu, Shiwu,Zhang, Shihui,Zeng, Jue,Li, Tingyu,Guo, Qinghua,&Jin, Shichao.(2020).A Framework for Land Use Scenes Classification Based on Landscape Photos.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,6124-6141. |
| MLA | Xu, Shiwu,et al."A Framework for Land Use Scenes Classification Based on Landscape Photos".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):6124-6141. |
入库方式： OAI收割
来源：植物研究所
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