基于稀疏表达的水体遥感反射率高光谱重构及其应用
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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12.175μg·L~(-1)减少至7.125μg·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).
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-2 A 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 situmeasured 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).
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-2 A 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 situmeasured 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).
引文
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[13]Lee Z,Shang S L,Hu C M,et al.Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors[J].Applied Optics,2014,53(15):3301-3310.
[14]郭宇龙,李云梅,朱利,等.基于HJ1A-CCD数据的高光谱影像重构研究[J].环境科学,2013,34(1):69-76.Guo Y L,Li Y M,Zhu L,et al.Research of hyperspectral reconstruction based on HJ1A-CCD data[J].Environmental Science,2013,34(1):69-76.
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[19]Li S T,Yang B.A new pan-sharpening method using a compressed sensing technique[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(2):738-746.
[20]Huang B,Song H H.Spatiotemporal reflectance fusion via sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):3707-3716.
[21]Elad M,Michal A.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[22]Chen Y,Nasrabadi N M,Tran T D.Hyperspectral image classification using dictionary-based sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):3973-3985.
[23]Xu M,Jia X P,Pickering M,et al.Cloud removal based on sparse representation via multitemporal dictionary learning[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(5):2998-3006.
[24]Shen F,Verhoef W,Zhou Y X,et al.Satellite estimates of wide-range suspended sediment concentrations in Changjiang(Yangtze)estuary using MERIS data[J].Estuaries and Coasts,2010,33(6):1420-1429.
[25]Li Y,Zhang Y L,Shi K,et al.Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery[J].Science of the Total Environment,2017,599-600:1705-1717.
[26]唐军武,田国良,汪小勇,等.水体光谱测量与分析Ⅰ:水面以上测量法[J].遥感学报,2004,8(1):37-44.Tang J W,Tian G L,Wang X Y,et al.The methods of water spectra measurement and analysis I:above-water method[J].Journal of Remote Sensing,2004,8(1):37-44.
[27]杜成功,李云梅,王桥,等.面向GOCI数据的太湖总磷浓度反演及其日内变化研究[J].环境科学,2016,37(3):862-872.Du C G,Li Y M,Wang Q,et al.Inversion model and daily variation of total phosphorus concentrations in Taihu Lake based on GOCI data[J].Environmental Science,2016,37(3):862-872.
[28]Lee Z,Carder K L,Mobley C D,et al.Hyperspectral remote sensing for shallow waters:2.Deriving bottom depths and water properties by optimization[J].Applied Optics,1999,38(18):3831-3843.
[29]Lee Z,Carder K L,Arnone R A.Deriving inherent optical properties from water color:a multiband quasi-analytical algorithm for optically deep waters[J].Applied Optics,2002,41(27):5755-5772.
[30]刘忠华,李云梅,吕恒,等.基于生物光学模型的巢湖后向散射概率估算[J].环境科学,2011,32(2):464-471.Liu Z H,Li Y M,LüH,et al.Estimating of backscattering rate in Lake Chaohu based on bio-optical model[J].Environmental Science,2011,32(2):464-471.
[31]Pope R M,Fry E S.Absorption spectrum(380-700 nm)of pure water.Ⅱ.Integrating cavity measurements[J].Applied Optics,1997,36(33):8710-8723.
[32]李云梅,黄家柱,韦玉春,等.用分析模型方法反演水体叶绿素的浓度[J].遥感学报,2006,10(2):169-175.Li Y M,Huang J Z,Wei Y C,et al.Inversing chlorophyll concentration of Taihu Lake by analytic model[J].Journal of Remote Sensing,2006,10(2):169-175.
[33]汪琪,马灵玲,唐伶俐,等.基于光谱稀疏模型的高光谱压缩感知重构[J].红外与毫米波学报,2016,35(6):723-730.Wang Q,Ma L L,Tang L L,et al.Hyperspectral compressive sensing reconstruction based on spectral sparse model[J].Journal of Infrared and Millimeter Waves,2016,35(6):723-730.
[34]Shi K,Zhang Y L,Zhu G W,et al.Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data[J].Remote Sensing of Environment,2015,164:43-56.
[35]He X Q,Bai Y,Pan D L,et al.Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters[J].Remote Sensing of Environment,2013,133:225-239.
[2]Shi K,Zhang Y L,Xu H,et al.Long-term satellite observations of microcystin concentrations in Lake Taihu during cyanobacterial bloom periods[J].Environmental Science&Technology,2015,49(11):6448-6456.
[3]尹球,巩彩兰,匡定波,等.湖泊水质卫星遥感方法及其应用[J].红外与毫米波学报,2005,24(3):198-202.Yin Q,Gong C L,Kuang D B,et al.Method of satellite remote sensing of lake water quality and its applications[J].Journal of Infrared and Millimeter Waves,2005,24(3):198-202.
[4]Palmer S C J,Kutser T,Hunter P D.Remote sensing of inland waters:challenges,progress and future directions[J].Remote Sensing of Environment,2015,157:1-8.
[5]Mc Clain C R.A decade of satellite ocean color observations[J].Annual Review of Marine Science,2009,1:19-42.
[6]Le C F,Li Y M,Zha Y,et al.A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes:the case of Taihu Lake,China[J].Remote Sensing of Environment,2009,113(6):1175-1182.
[7]Gitelson A A,Dall'Olmo G,Moses W,et al.A simple semianalytical model for remote estimation of chlorophyll-a in turbid waters:validation[J].Remote Sensing of Environment,2008,112(9):3582-3593.
[8]Huang C C,Zou J,Li Y M,et al.Assessment of NIR-red algorithms for observation of chlorophyll-a in highly turbid inland waters in China[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,93:29-39.
[9]Zheng Z B,Li Y M,Guo Y L,et al.Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake,China[J].Remote Sensing,2015,7(10):13975-13999.
[10]Sun D Y,Qiu Z F,Hu C M,et al.A hybrid method to estimate suspended particle sizes from satellite measurements over Bohai Sea and Yellow Sea[J].Journal of Geophysical Research:Oceans,2016,121(9):6742-6761.
[11]宋挺,周文鳞,刘军志,等.利用高光谱反演模型评估太湖水体叶绿素a浓度分布[J].环境科学学报,2017,37(3):888-899.Song T,Zhou W L,Liu J Z,et al.Evaluation on distribution of chlorophyll-a content in surface water of Taihu Lake by hyperspectral inversion models[J].Acta Scientiae Circumstantiae,2017,37(3):888-899.
[12]Sun D Y,Hu C M,Qiu Z F,et al.Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes:test of a new scheme and implications for inversion algorithms[J].Optics Express,2015,23(11):A718-A740.
[13]Lee Z,Shang S L,Hu C M,et al.Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors[J].Applied Optics,2014,53(15):3301-3310.
[14]郭宇龙,李云梅,朱利,等.基于HJ1A-CCD数据的高光谱影像重构研究[J].环境科学,2013,34(1):69-76.Guo Y L,Li Y M,Zhu L,et al.Research of hyperspectral reconstruction based on HJ1A-CCD data[J].Environmental Science,2013,34(1):69-76.
[15]Aharon M,Elad M,Bruckstein A.rm K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[16]Wright J,Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[17]Yang J C,Wright J,Huang T S,et al.Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[18]Arad B,Ben-Shahar O.Sparse recovery of hyperspectral signal from natural RGB images[A].In:Leibe B,Matas J,Sebe N,et al.European Conference on Computer Vision[M].Cham,Switzerland:Springer,2016.19-34.
[19]Li S T,Yang B.A new pan-sharpening method using a compressed sensing technique[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(2):738-746.
[20]Huang B,Song H H.Spatiotemporal reflectance fusion via sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):3707-3716.
[21]Elad M,Michal A.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[22]Chen Y,Nasrabadi N M,Tran T D.Hyperspectral image classification using dictionary-based sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):3973-3985.
[23]Xu M,Jia X P,Pickering M,et al.Cloud removal based on sparse representation via multitemporal dictionary learning[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(5):2998-3006.
[24]Shen F,Verhoef W,Zhou Y X,et al.Satellite estimates of wide-range suspended sediment concentrations in Changjiang(Yangtze)estuary using MERIS data[J].Estuaries and Coasts,2010,33(6):1420-1429.
[25]Li Y,Zhang Y L,Shi K,et al.Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery[J].Science of the Total Environment,2017,599-600:1705-1717.
[26]唐军武,田国良,汪小勇,等.水体光谱测量与分析Ⅰ:水面以上测量法[J].遥感学报,2004,8(1):37-44.Tang J W,Tian G L,Wang X Y,et al.The methods of water spectra measurement and analysis I:above-water method[J].Journal of Remote Sensing,2004,8(1):37-44.
[27]杜成功,李云梅,王桥,等.面向GOCI数据的太湖总磷浓度反演及其日内变化研究[J].环境科学,2016,37(3):862-872.Du C G,Li Y M,Wang Q,et al.Inversion model and daily variation of total phosphorus concentrations in Taihu Lake based on GOCI data[J].Environmental Science,2016,37(3):862-872.
[28]Lee Z,Carder K L,Mobley C D,et al.Hyperspectral remote sensing for shallow waters:2.Deriving bottom depths and water properties by optimization[J].Applied Optics,1999,38(18):3831-3843.
[29]Lee Z,Carder K L,Arnone R A.Deriving inherent optical properties from water color:a multiband quasi-analytical algorithm for optically deep waters[J].Applied Optics,2002,41(27):5755-5772.
[30]刘忠华,李云梅,吕恒,等.基于生物光学模型的巢湖后向散射概率估算[J].环境科学,2011,32(2):464-471.Liu Z H,Li Y M,LüH,et al.Estimating of backscattering rate in Lake Chaohu based on bio-optical model[J].Environmental Science,2011,32(2):464-471.
[31]Pope R M,Fry E S.Absorption spectrum(380-700 nm)of pure water.Ⅱ.Integrating cavity measurements[J].Applied Optics,1997,36(33):8710-8723.
[32]李云梅,黄家柱,韦玉春,等.用分析模型方法反演水体叶绿素的浓度[J].遥感学报,2006,10(2):169-175.Li Y M,Huang J Z,Wei Y C,et al.Inversing chlorophyll concentration of Taihu Lake by analytic model[J].Journal of Remote Sensing,2006,10(2):169-175.
[33]汪琪,马灵玲,唐伶俐,等.基于光谱稀疏模型的高光谱压缩感知重构[J].红外与毫米波学报,2016,35(6):723-730.Wang Q,Ma L L,Tang L L,et al.Hyperspectral compressive sensing reconstruction based on spectral sparse model[J].Journal of Infrared and Millimeter Waves,2016,35(6):723-730.
[34]Shi K,Zhang Y L,Zhu G W,et al.Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data[J].Remote Sensing of Environment,2015,164:43-56.
[35]He X Q,Bai Y,Pan D L,et al.Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters[J].Remote Sensing of Environment,2013,133:225-239.
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