Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method
文献类型：期刊论文
| 作者 | Li, Dacheng1,2 ; Cui, Fangxiao1; Wang, Anjing1; Li, Yangyu1; Wu, Jun1; Qiao, Yanli1
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2020-12-01 |
| 卷号 | 58 |
| ISSN号 | 0196-2892 |
| 关键词 | Atmospheric measurements Clouds Atmospheric modeling Brightness temperature Feature extraction Remote sensing Brightness temperature spectrum least absolute shrinkage and selection operator (LASSO) longwave infrared (LWIR) remote sensing |
| DOI | 10.1109/TGRS.2020.2989526 |
| 通讯作者 | Cui, Fangxiao(fxcui@aiofm.ac.cn) |
| 英文摘要 | Longwave infrared (LWIR) spectroscopy is useful for detecting and identifying hazardous clouds by passive remote sensing technology. Gaseous constituents are usually assumed to be thin plumes in a three-layer model, from which the spectral signatures are linearly superimposed on the brightness temperature spectrum. However, the thin-plume model performs poorly in cases of thick clouds. A modification to this method is made using synthetic references as target spectra, which allow linear models to be used for thick clouds. The prior background, which is generally unknown in most applications, is reconstructed through a regression method using predefined references. However, large residuals caused by fitting errors may distort the extracted spectral signatures and identification results if the predefined references are not consistent with the real spectral shapes. A group of references are generated to represent the possible spectral shapes, and the least absolute shrinkage and selection operator (LASSO) method is used to select the most appropriate reference for spectral fitting. Small residuals and adaptive identification are achieved by automatically selecting the reference spectrum. Two experiments are performed to verify the algorithm proposed in this article. Ethylene is adaptively detected during an indoor release process, and the spectral shape varies with the amount released. In addition, ammonia is measured under different humidity conditions, and the background is adaptively removed using the LASSO method. Based on this research, LWIR remote sensing technology can be applied in various target-detection scenarios, and adaptive identification is achieved to promote hazardous cloud detection. |
| WOS关键词 | REGRESSION SHRINKAGE ; BAND SELECTION |
| 资助项目 | National Natural Science Foundation of China[41505020] ; Laboratory Innovation Foundation of Chinese Academy of Sciences[CXJJ-19S002] ; Key Deployment Project Foundation Chinese Academy of Sciences[KGFZD-135-16-002-2] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000594389800031 |
| 资助机构 | National Natural Science Foundation of China ; Laboratory Innovation Foundation of Chinese Academy of Sciences ; Key Deployment Project Foundation Chinese Academy of Sciences |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/105433]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Cui, Fangxiao |
| 作者单位 | 1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Opt Calibrat & Characterizat, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Dacheng,Cui, Fangxiao,Wang, Anjing,et al. Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58. |
| APA | Li, Dacheng,Cui, Fangxiao,Wang, Anjing,Li, Yangyu,Wu, Jun,&Qiao, Yanli.(2020).Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58. |
| MLA | Li, Dacheng,et al."Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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