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自然资源遥感  2023, Vol. 35 Issue (4): 17-24    DOI: 10.6046/zrzyyg.2022349
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
面向地质应用的ZY-1 02D高光谱数据大气校正方法对比
李娜1(), 董新丰1,2(), 王靖岚3, 陈理1, 甘甫平1, 李彤彤2, 张世凡2
1.中国自然资源航空物探遥感中心,北京 100083
2.中国地质大学(北京)地球科学与资源学院,北京 100083
3.四川水利职业技术学院,成都 610000
Comparative study on atmospheric correction methods for ZY-1 02D hyperspectral data for geological applications
LI Na1(), DONG Xinfeng1,2(), WANG Jinglan3, CHEN Li1, GAN Fuping1, LI Tongtong2, ZHANG Shifan2
1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2. School of Earth Sciences and Resource,China University of Geosciences(Beijing),Beijing 100083, China
3. Sichuan Water Conservancy Vocational College,Chengdu 610000, China
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摘要 

高光谱数据凭借其在光谱维的技术优势,在地物精细识别尤其是矿物信息精准识别方面应用广泛。高光谱反射率产品是开展矿物识别的基础数据依据,使用合适的大气校正方法获取能够满足应用需求的高精度的地表反射率产品至关重要。为此,采用ATCOR,FLAASH和QUAC这3种应用相对较广的大气校正模型,对资源一号02D(ZY-1 02D)卫星高光谱数据进行大气校正处理,并从目视效果、典型地物波谱分析和矿物信息提取3个方面开展了对比分析。分析结果表明: 在目视效果上,3种大气校正模型均能有效提升影像清晰度,ATCOR模型略优于FLAASH和QUAC模型; 3种模型典型地物光谱与ASD实测光谱相关系数平均值R2达0.7以上,吻合度较好,精度较高,ATCOR模型反演结果的影像光谱更接近ASD实测光谱; 对绿泥石识别结果三者一致性较好,绢云母一致性则稍差,对比发现FLAASH和QUAC模型在地表绢云母含量较低区域漏识率较高。综上所知,3种模型大气校正效果均比较理想,但在矿物识别应用中ATCOR模型较FLAASH和QUAC模型总体上有优势。

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李娜
董新丰
王靖岚
陈理
甘甫平
李彤彤
张世凡
关键词 资源一号02D大气校正ATCORFLAASHQUAC矿物识别    
Abstract

Hyperspectral data, exhibiting technical advantages in the spectral dimension, have been extensively used for accurately identifying surface features, particularly mineral information. Mineral identification relies on hyperspectral reflectance products, necessitating the application of proper atmospheric correction methods to obtain high-precision surface reflectance products that meet application requirements. Hence, three commonly used atmospheric correction models, ATCOR, FLAASH, and QUAC, were utilized to correct the hyperspectral data acquired by the ZY-1 02D satellite. Moreover, a comparative analysis was conducted on their visual effects, spectral analysis of typical surface features, and extraction of mineral information. The results are as follows: ① All three atmospheric correction models can effectively enhance image clarity in terms of visual effects. Specifically, the ATCOR model slightly outperformed the FLAASH and QUAC models; ② The correlation coefficients (R2) between the typical surface feature spectra of the three models and the ASD-measured spectra showed average values exceeding 0.7, suggesting high consistency and accuracy. Especially, the imaging spectra derived from the inversion results of the ATCOR model were more similar to the ASD-measured spectra; ③ The three models yielded relatively consistent results in chlorite identification but divergent results in sericite identification. Comparatively, the FLAASH and QUAC models exhibited high omission rates in surface regions with low sericite content. Overall, all three models can achieve satisfactory atmospheric correction effects, but the ATCOR model is superior to the other two models in mineral identification.

Key wordsZY-1 02D    atmospheric correction    ATCOR    FLAASH    QUAC    mineral identification
收稿日期: 2022-08-30      出版日期: 2023-12-21
ZTFLH:  TP79  
  P627  
基金资助:国家重点研发计划项目“中空间分辨率光谱地球研发与应用技术研究”(2019YFE0127300);民用航天项目(D040102);中国地质调查局项目“全国遥感地质调查与监测”(DD20221642)
通讯作者: 董新丰(1986-),男,博士研究生,高级工程师,主要从事高光谱遥感地学应用研究。Email: dongxinfeng229@163.com
作者简介: 李娜(1989-),女,硕士,工程师,主要从事高光谱遥感研究。Email: 942750607@qq.com
引用本文:   
李娜, 董新丰, 王靖岚, 陈理, 甘甫平, 李彤彤, 张世凡. 面向地质应用的ZY-1 02D高光谱数据大气校正方法对比[J]. 自然资源遥感, 2023, 35(4): 17-24.
LI Na, DONG Xinfeng, WANG Jinglan, CHEN Li, GAN Fuping, LI Tongtong, ZHANG Shifan. Comparative study on atmospheric correction methods for ZY-1 02D hyperspectral data for geological applications. Remote Sensing for Natural Resources, 2023, 35(4): 17-24.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022349      或      https://www.gtzyyg.com/CN/Y2023/V35/I4/17
Fig.1  研究区范围
Fig.2  3种模型大气校正后真彩色影像
(ZY-1 02D B29(R),B19(G),B8(B)真彩色合成影像)
模型 地物1 地物2 地物3 地物4 地物5 地物6 平均值 标准差
FLAASH 0.937 0.636 0.633 0.939 0.979 0.985 0.851 5 0.154 5
QUAC 0.791 0.372 0.340 0.893 0.954 0.949 0.716 5 0.260 6
ATCOR 0.926 0.775 0.775 0.954 0.962 0.965 0.892 8 0.084 3
Tab.1  典型地物影像光谱与ASD光谱相关系数对比分析
Fig.3  地物验证点ASD光谱与大气校正后光谱对比
Fig.4  3种模型大气校正矿物信息提取
Fig.5  影像光谱与USGS光谱及ASD光谱对比
Fig.6  3种模型大气校正绢云母浓度
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