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基于稀疏表达的水体遥感反射率高光谱重构及其应用
摘要点击 198  全文点击 55  投稿时间:2018-04-25  修订日期:2018-07-06
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中文关键词  稀疏表达  光谱重构  遥感反射率  太湖  杭州湾
英文关键词  sparse representation  hyperspectral reconstruction  remote sensing reflectance  Lake Taihu  Hangzhou Bay
作者单位E-mail
李渊 浙江工商大学旅游与城乡规划学院, 杭州 310018
中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008 
liyuannjnu@163.com 
李云梅 南京师范大学地理科学学院, 南京 210023 liyunmei@njnu.edu.cn 
郭宇龙 河南农业大学资源与环境学院, 郑州 450002  
张运林 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008  
张毅博 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008  
胡耀躲 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008  
夏忠 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008  
中文摘要
      高光谱重构技术可以有效地突破多光谱卫星传感器波段设置的限制,获得更多更有效的地物光谱信息.本研究基于稀疏表达方法提出了一种针对水体遥感反射率的高光谱重构算法,以太湖、杭州湾的原位水体光谱数据为数据源,在5种常用水色传感器(Sentinel-2A MSI、MERIS、MODIS Aqua、GOCI以及ⅦRS)上进行了高光谱重构实验,最后将该算法应用于GOCI数据,进行了算法适用性验证.结果表明:①基于稀疏表达的高光谱重构算法可以在不利用实测光谱数据的情况下实现高光谱重构,光谱重构精度高于多元回归光谱重构算法;②基于稀疏表达的高光谱重构算法在5种水色传感器上都取得了较好的效果,平均相对误差均在10%以下,均方根误差均在0.005 sr-1以下;③相比于原始GOCI多光谱数据,经稀疏表达高光谱重构后的GOCI数据在叶绿素a浓度和总悬浮物浓度估算精度上有不同程度提升.其中对叶绿素a浓度估算而言,平均相对误差从80.6%减少至51.5%,均方根误差从12.175 μg·L-1减少至7.125 μg·L-1;对悬浮物浓度估算而言,平均相对误差从19.1%减少至18.8%,均方根误差从29.048 mg·L-1减少至28.596 mg·L-1.
英文摘要
      Multispectral satellite sensors have several limitations with respect to capturing the target's spectral information due to their band setting and number of bands. The hyperspectral reconstruction technique is an effective method to obtain hyperspectral information from multispectral data. In this study, we propose a hyperspectral reconstruction algorithm based on the sparse representation of water remote sensing reflectance. The proposed algorithm was validated for five ocean color sensors (Sentinel-2A MSI, MERIS, MODIS Aqua, GOCI, and ⅦRS) using in situ measured above-water remote sensing reflectance. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of the reconstructed and measured spectra for five ocean color sensors were less than 10% and 0.005 sr-1, respectively. Compared with the spectra reconstruction algorithm based on multi-variable linear regression, the proposed algorithm can obtain the features of complex water remote sensing reflectance without using in situ-measured reflectance for algorithm tuning. In addition, the accuracy of the proposed algorithm is better than the spectra reconstruction algorithm based on multi-variable linear regression. Two spectra reconstruction algorithms were applied to five ocean color sensors to test the applicability of the remotely estimated water constituent concentration. The statistical results for the reconstructed spectral factors and in situ water constituent concentration suggest that the reconstructed reflectance derived by the proposed algorithm has a performance similar to that of in situ-measured hyperspectral reflectance. The reconstructed reflectance derived by the proposed algorithm performs better than the spectra reconstruction algorithm based on multi-variable linear regression. Finally, the proposed algorithm was applied to GOCI data to remotely estimate the chlorophyll-a and total suspended matter concentrations. The accuracy of the water constituent concentration estimated from reconstructed images is better than that using original multispectral images. For the estimation of the chlorophyll-a concentration, the MAPE improved from 80.6% to 51.5% and the RMSE improved from 12.175 μg·L-1 to 7.125 μg·L-1. For the estimation of total suspended matter, the MAPE improved from 19.1% to 18.8% and the RMSE improved from 29.048 mg·L-1 to 28.596 mg·L-1.

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