基于Hyperion影像的高光谱水汽含量反演的算法比较研究

基于高光谱遥感图像数据的大气参数反演和一体化辐射校正具有重要研究意义和应用价值。首先,通过6S模型辐射传输计算分析了EO-1/Hyperion遥感影像在940和1 130nm附近水汽吸收区域的光谱吸收特点。其次,采用两通道比值法和三通道比值法,比较了不同波段组合的大气含水量高光谱遥感反演精度并进行了敏感性分析,模拟实验结果表明采用三波段比值算法的相关系数和均方根误差均优于对应的两波段算法。最后,利用张掖地区2008年3景EO-1Hyperion高光谱遥感影像,反演了大气含水量,并与地基CE-318太阳分光光度计测量数据进行对比验证,结果表明:1 124nm水汽吸收通道反演精度优于940nm,两通道和三通道比值法的均方根误差分别为0.369和0.128g/cm2,三通道比值方法优于两通道比值方法,与地面观测结果一致。     

基于Landsat 8 TIRS数据的大气水汽含量反演劈窗算法

Landsat 8是2013年最新发射的Landsat卫星,携带了OLI和TIRS两个传感器,其中TIRS传感器获取了两个临近的热红外通道信息。劈窗协方差—方差比算法(SWCVR)是一种最通用的基于热红外的大气水汽含量反演方法,利用两个热红外通道(其中一个在大气窗口,另一个在大气吸收谱段)的吸收差异来反演大气水汽含量,该方法已经在MODIS等中低分辨率(1km)的热红外数据上得到很好的应用。将SWCVR算法移植到较高分辨率的Landsat 8TIRS数据上,并对水汽含量反演结果进行精度验证。气象数据验证结果表明,水汽含量的反演精度可以达到0.43g/cm~2。用MODIS水汽产品(MOD05)做交叉验证,反演的水汽含量和MOD05水汽含量的均方根误差(RMSE)为0.44g/cm~2,平均绝对误差(MAE)为0.34g/cm~2。总的来说,SWCVR算法应用于Landsat 8数据的水汽含量反演也能得到一个较高的精度。     

陆地资源卫星数据地表温度反演

针对目前陆地资源卫星(Landsat-8)地表温度反演过程中,地表比辐射率估计和敏感度分析中存在的不足,对这两方面进行改进,提出了一种基于Landsat-8数据的地表温度反演算法。该文主要从劈窗算法的推导、参数的估计、敏感度分析等方面进行研究。对于大气透过率的计算,首先用与其有相邻过境时间的MODIS数据反演大气水汽含量,然后通过中分辨率的大气传输模型(Moderate Resolution Atmospheric Transmission,MODTRAN)模拟大气水汽含量与透过率的关系,最后得到大气透过率。对于发射率的计算,通过分类和ASTER提供的光谱库获得。将大气辐射传输方程模拟的地表温度与此劈窗算法反演的地表温度做比较,结果表明平均精度达到0.82K。最后研究了大气水汽含量对地表温度的影响。结果显示,当大气水汽含量误差为0.1g/cm2,其对温度反演精度的影响最大不超过0.3K;当大气水汽含量的反演误差较大的时候,其对温度反演精度的影响较大。     

高光谱图像大气校正自动化与快速处理北大核心CSCD

全谱段宽幅高分辨率推扫式光谱成像仪作为我国新一代航空高光谱成像仪已进入应用校飞阶段,文章针对其TB/日数量级高光谱图像快速处理问题,对大气校正过程的参数自动化设置方法和并行加速方法进行研究。在传统基于辐射传输模型的大气校正基础上,分析了可见近红外和短波红外通道是否波段合并、重合波段优选、大气类型选择、是否水汽反演、水汽吸收波段选择等方面对反射率反演精度的影响,实现了参数优化自动设置,并开发了并行化大气校正算法。以吉林榆树和辽宁辽中的数据进行验证,结果表明,反演反射率与参考真值反射率一致性高,同时处理速度比串行处理大大提高,可为高光谱反射率数据产品的业务化生产提供有力工具。     

多波段微波辐射计反演大气温湿廓线性能分析

为了为星载、机载以及地基微波大气温湿廓线探测仪通道的设置、大气参数反演指标的论证、反演算法的开发以及反演产品的质量评定提供参考依据,基于快速辐射传输模式(RTTOV10)和大气参数廓线库,建立了基于神经网络的微波大气温湿廓线反演性能分析方法,分析了反演方法、通道选择、亮温观测误差和地表比辐射率等因素对大气温湿廓线反演性能的影响。模拟试验分析表明:1神经网络反演算法显著优于线性统计回归反演算法,特别是对亮温观测噪声的敏感性相对较弱;2183.31GHz附近的水汽探测通道能够为大气温度廓线反演提供一定的信息;118.75GHz附近的温度探测通道对整个大气的温度反演均有明显影响,在200hPa附近误差的影响量达0.4K;350~60GHz和118.75GHz附近的温度探测通道对基于183.31GHz附近通道的湿度廓线反演具有重要影响,而且存在一定的互补性;4微波亮温观测误差以及地表比辐射率假定对大气温湿廓线反演有着显著影响。     

FY-3近红外与热红外资料大气柱水汽总量反演对比

大气水汽含量信息对卫星图像辐射校正、大气微物理过程理解、降水预报等具有重要意义。分别利用FY-3A中分辨率成像光谱仪(MERSI)近红外和扫描辐射计(VIRR)热红外通道反演大气柱水汽总量,并将反演结果与地面探空站观测值进行比对分析,结果表明:(1)MERSI反演值与观测值的相关系数为0.763,而VIRR反演值与观测值的相关性较差,相关系数为0.169;从水汽含量的反演精度看,MERSI(RMSE=1.109g/cm~2)高于VIRR(RMSE=1.894g/cm~2);(2)MERSI三通道水汽反演精度比17、18、19通道(RMSE分别为1.133、1.424和1.827g/cm~2)高,主要原因是3个水汽通道对水汽敏感性不同,综合利用3个通道反演大气柱水汽含量可达到取长补短的效果。     

EO-1 Hyperion高光谱数据的预处理

针对EO-1 Hyperion高光谱遥感数据的特点，在图像质量检查的基础上，对Hyperion图像进行了未定标和受水汽影响波段的去除、坏线修复、条纹去除、Smile效应降低、大气纠正等预处理，获得了较好质量的图像，为图像的进一步分析和实际应用提供了保障。结果表明图像大气纠正后光谱优化处理能进一步提高图像的质量。     

基于719 nm水汽吸收波段的日光诱导叶绿素荧光反演研究

日光诱导叶绿素荧光(SIF)是一种植物光合作用直接探测新方法。目前O_2-A和O_2-B吸收线波段的叶绿素荧光填充效应被广泛应用于探测近红外(760 nm)和红光波段(687 nm)的植被冠层SIF信号。SIF光谱范围为650～800 nm,虽然水吸收波段(719 nm)介于叶绿素荧光发射峰值690 nm和740 nm之间,且具备较强的光谱吸收特征,但该水汽吸收光谱特征尚未应用于冠层SIF探测,因此,基于模型模拟和野外实验观测数据,使用夫琅禾费暗线SIF反演法,评价了基于719 nm波段水吸收波段的SIF反演潜力,其中野外光谱数据采用ASD FieldSpec Pro便携式地物光谱仪(3 nm分辨率)测量。首先,利用FLD、3FLD、iFLD等3种经典的SIF反演方法,检验和对比分析了719水汽吸收波段的SIF反演性能,结果表明使用水吸收线比使用O_2-B吸收线表现更优,反演RMSE为0.154 W/m~2/μm/sr。其次,定量计算了水汽和氧气吸收波段SIF反演的敏感度和不确定性,结果表明,719水汽吸收波段与O_2-B吸收线相比,其吸收线内外的反射率和荧光比值估算误差对SIF反演误差的贡献更小,但是显大于比02-A波段。最后,利用野外多角度和日变化观测实验数据,检验和分析了三个大气吸收波段的SIF反演结果,发现719 nm水吸收波段的冠层SIF与O_2-A和O_2-B氧气吸收波段具有相似的角度变化和日变化特征,表现为后视和热点方向的SIF高、前视和暗点方向的SIF低,以及中午SIF高、早晚SIF低。研究表明利用719 nm波段的水汽吸收波段的光谱信息,可以准确反演近地面冠层SIF信号,研究结果为近地面冠层SIF观测提供了一个新的波段。     

卫星红外高光谱反演大气水汽含量

利用高光谱大气红外探测仪AIRS模拟及观测数据,发展基于主成分分析技术的多层前馈神经网络反演算法,进行大气中水汽柱总量(IWV)的反演计算、模拟及实测验证。首先,基于全球晴空大气廓线训练样本SeeBorV4.0,利用快速辐射传输模式CRTM进行了辐射传输模拟计算,得到全球高光谱分辨率模拟辐亮度;其次,利用主成分分析技术对模式模拟和AIRS实测高光谱数据进行降维、去噪及去相关处理,并采用多层前向神经网络算法反演大气水汽柱总量;最后,利用数值试验、AIRS实测L1B数据及其水汽产品,对反演算法进行了验证。通过与AIRS官方大气产品的统计分析,本算法反演均方根误差为0.387 g/cm²,最大偏差为0.82 g/cm²,空间分辨率保留了AIRS像素原分辨率(比AIRS官方大气产品高3倍)。     

资源一号02D高光谱数据大气校正及应用北大核心CSCD

针对高光谱数据大气校正耗时长和查找表构建不准确等问题,提出基于MODTRAN辐射传输模型实时创建大气校正参数查找表的方法,并应用于水体叶绿素浓度反演。首先,基于高光谱数据实时构建大气校正参数查找表;其次,根据循环迭代反演得到水汽含量和气溶胶光学厚度对查找表插值得到各个波段的大气校正参数,从而完成所有波段数据的大气校正;最后,选择植被、土壤和水体3类典型地物精度分析,并基于反演水体的叶绿素a浓度验证大气校正精度的可靠性。实验结果表明:该方法明显优于6S、FLAASH等大气校正方法;在运行效率上,在多线程并行加速后,运行效率提升了2~4倍;基于水体反射率数据反演水体叶绿素a浓度,反演模型预测集验证中ρ为0.804 7,RMSE为1.8。     

Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping

A comparison of the performance of three feature extraction methods was made for mapping forest crown closure (CC) and leaf area index (LAI) with EO-1 Hyperion data. The methods are band selection (SB), principal component analysis (PCA) and wavelet transform (WT). Hyperion data were acquired on October 9, 2001. A total of 38 field measurements of CC and LAI were collected on August 10-11, 2001, at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) conducting atmospheric correction with High Accuracy Atmospheric Correction for Hyperspectral Data (HATCH) to retrieve surface reflectance, (2) extracting features with the three methods: SB, PCA and WT, (3) establishing multivariate regression prediction models, (4) predicting and mapping pixel-based CC and LAI values, and (5) validating the CC and LAI mapped results with photo-interpreted CC and LAI values. The experimental results indicate that the energy features extracted by the WT method are the most effective for mapping forest CC and LAI (mapped accuracy (MA) for CC=84.90%, LAI MA=75.39%), followed by the PCA method (CC MA=77.42%, LAI MA=52.36%). The SB method performed the worst (CC MA=57.77%, LAI MA=50.87%).     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping

Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (～7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82–2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noise ratio, the presence of sensor-specific defects and thicker atmospheric column due to its spaceborne platform makes certain environmental and geological applications difficult or impossible. In this article we demonstrate these calibration-related uncertainties, which are manifest from the preprocessing stage to the classification stage. In addition, the intimate mixing of minerals within specific targets, for example within individual outcropping lithological units or endmembers, adds uncertainty to our spectral discrimination results. The aim of this study was to develop and evaluate an approach for geological mapping of outcrops with Earth Observing-1 (EO-1) Hyperion data. Atmospheric corrections and correction for cross-track illumination (CTI) variations (smile) were determined at different wavelength regions: the visible–near-infrared (VNIR; 420–1000 nm) and shortwave infrared (SWIR; 1000–2400 nm) regions. Our methodology was tested in a selected site at Central Anatolia, Turkey containing minimal vegetation cover. The results obtained from the image analyses were then compared and assessed with field observations and spectral measurements.     

Atmospheric correction of Hyperion imagery over estuarine waters: a case study of the Pearl River Estuary in southern China

Atmospheric correction of ocean colour remote-sensing data is based on the assumption that no water-leaving reflectance occurs in the near-infrared (NIR) area. However, this assumption is not valid for highly productive waters. To solve this problem, this paper describes a modified atmospheric correction scheme for Hyperion data. Based on the assumption that the ratio of water-leaving radiance and aerosol radiance in two NIR bands follows a fixed rule, we moved a rectangular box around the imagery to calculate those two parameters, which were then used to replace the assumption of zero water-leaving radiance in the NIR. We applied the new atmospheric correction algorithm to one Hyperion image. Following comparison of in situ measurements to results of the FLAASH atmospheric correction schema, preliminary findings show that the new algorithm is effective in reducing error in retrieved water-leaving radiance values, to some extent.     

海岸带高光谱遥感与近海高光谱成像仪(HICO)

应海岸带监测需求,高光谱成像仪开始在海岸带监测中发挥重要作用。搭载于国际空间站上的HICO(Hyperspectral Imager for the Coastal Ocean)是第一颗针对近岸海洋遥感的高光谱成像仪,其波谱范围为360～1 080 nm,光谱分辨率为5 nm。介绍了HICO数据的基本情况,并与在轨星载高光谱成像仪EO-1 Hyperion和HJ-1A HSI基本参数做了对比。同时针对高浑浊水体,以黄河三角洲近岸3种典型地物为例,结合FLAASH大气校正模型,提取了辐亮度和地表反射率,初步对比分析了HICO和HSI的光谱性能。结果表明HICO能更好地反映近岸地物的光谱特征。     

基于MODIS 影像数据的劈窗算法研究及其参数确定

劈窗算法是目前由热红外遥感数据获取陆面温度的主要方法。在介绍劈窗算法的一般表现形式的基础上, 我们推导出适合于MOD IS 影像数据的劈窗算法。大气透过率和地表比辐射率是求解地表温度的两个关键参数。由于MOD IS 图像分辨率较低,MOD IS 像元主要由水面、植被和裸土3种地物类型构成, 故可依据这3 种地物的构成比例确定地表比辐射率。从遥感影像上反演大气的水汽含量, 再根据大气水汽含量与大气透过率的关系计算出大气透过率。最后将文中推导的劈窗算法用于江苏省地表温度的反演。反演出来的地表温度图显示出明显的地表温度空间差异、城市热岛效应和不同的地物类型。     

Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters

Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of Park and Ruddick (2005) to take account of bidirectional effects.Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670-750 nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667 nm and 678 nm and for MERIS bands 665 nm and 681 nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.     

基于量子粒子群优化的改进的模糊C-均值聚类算法

模糊C均值算法（FCM）是一种用于聚类的最流行的技术。不过,传统的FCM使用欧氏距离作为数据集的相似准则,从而导致数据集的划分有相等的趋势。而数据集的形状和簇的密度对聚类性能有高度影响。为了解决这个问题,提出基于簇密度的距离调节因子以修正相似性度量。同时,针对模糊C-均值（FCM）聚类算法对初始聚类中心选择敏感,易陷入局部最优的问题,采用量子粒子群优化算法以获取全局最优解。仿真实验证明,改进的聚类算法（QPSO-FCM-CD）具有良好的性能。     

The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data

In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized.