Hyperion高光谱遥感数据大气校正方法

由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节;通过应用大气校正模块FLAASH,研究选择了合适的大气模式、水汽含量、气溶胶模型、波谱分辨率和多散射模型等参数,对内蒙东胜地区Hyperion高光谱遥感影像进行大气校正;比较了校正前后典型地物的光谱曲线,并将它们与实验室典型地物光谱曲线进行对比,大气校正后得到的光谱曲线和实验室得到的光谱曲线具有较好的一致性,达到了去除大气影响的目的,同时校正生成的水汽分布也表明校正效果良好。     

一种多光谱遥感影像与航拍影像融合算法

无人机航拍影像空间分辨率高,纹理信息丰富,但其光谱信息匮乏,不利于遥感信息解译。为此提出一种基于脉冲耦合神经网络模型的融合算法,通过计算非规则区域的统计特性,将无人机航拍影像的亚米级高空间分辨率信息注入到遥感卫星多光谱影像中,以获取具有亚米级空间分辨率和高的光谱分辨率的遥感融合影像。通过定性和定量的对比实验,表明该算法优于经典的遥感影像融合方法,同时验证了其在减小光谱扭曲和空间纹理细节保持等方面的有效性。     

GF-5卫星高光谱数据大气校正反射率精度评价

反射率是高光谱遥感数据应用的基础,直接关系到高光谱应用效能和质量。目前,对国产GF-5卫星高光谱数据的精确大气校正反射率精度评价方法尚未有全面深入的研究,这严重制约了国产高光谱遥感数据的高质量应用。针对此问题,综合采用6S模型和FLAASH模块,选取了三个实验区的三种典型地物及外业光谱数据,采用三种定量化指标进行大气校正,得出了以下结论:三种地物大气校正反射率与实测反射率曲线特征基本一致,黑土地的大气校正反射率光谱最优,水体由于反射率数值较低,大气校正反射率光谱稍差;可见光近红外波段大气校正效果优于短波红外波段;6S模型大气校正结果略优于FLAASH模块,更适用于GF-5卫星高光谱影像。     

改进的多光谱遥感影像超分辨率重构算法

针对现有遥感影像重构算法数据资源有限、配准精度低等问题,结合遥感影像的光谱特征,提出一种改进的多光谱遥感影像超分辨率重构算法。提取场景结构特征作为重构的正则化约束条件,保持重构结果中的高频信息。利用波段间的交叉相关,获得场景的结构特征信息。通过迭代反投影算法对单波段影像进行重构,将其合成为全色高分辨率遥感影像。仿真实验结果表明,该算法的重构效果较优。     

大气探测傅里叶变换光谱仪

高精度的大气红外光谱遥感是实现数值天气预报、进行环境监测等应用的关键技术。论述了星载大气探测傅里叶变换光谱系统在完成这一高精度遥感测量中的重要地位及其发展与现状,从应用的角度提出了大气探测傅里叶变换光谱仪器光谱波段、分辨率等技术指标的要求,讨论了仪器的总体设计方案,提出了成功地进行大气傅里叶光谱探测所必须解决的关键技术。     

遥感数字图像的大气辐射校正应用研究

卫星遥感数字图像成像过程中，由于电磁波受大气作用造成数据质量下降，影响遥感信息的提取及精度。介绍了大气辐射校正的一般原理和PCI软件ATCOR2模块的算法，研究了基于该模块的遥感数字图像大气辐射校正实现方法。根据研究区实际情况，选用1976年美国标准大气乡村气溶胶大气参数，对一景Landsat TM影像进行校正处理。对比处理前后图像和直方图，可知该方法增强了图像清晰度，提高了视觉效果，有利于遥感信息提取和专题解译。     

基于遥感卫星图像的ATCOR2快速大气纠正模型及应用

在卫星遥感成像过程中,由于气溶胶和大气中分子吸收和散射的作用,造成数据质量下降甚至变化,以至于极大地影响着遥感信息的提取和参数反演的精度。越来越多的研究表明:大气纠正将成为遥感理论和应用研究中的重要组成部分,能极大地提高卫星遥感数据在质量和时间系列的完整性。介绍一种适用于空间分布的快速大气纠正模型ATCOR2(A Spatially-Adaptive FastAtmospheric Correction-2),详细介绍模型的算法、模块结构和计算流程,讨论参数文件的结构和意义,并通过一个实例给出ATCOR2模型运行的结果。     

基于GA-BP算法的多分辨率遥感影像融合技术

由于Landsat-5唯一的热红外波段遥感影像TM6的空间分辨率不高,使得其应用与研究程度远不及其它波段广泛。为此,运用GA-BP算法来提高TM6遥感影像的空间分辨率,并进行仿真实验,结果表明:①GA-BP算法有效地避免了BP算法陷入局部最小点、收敛速度慢的问题,是一种快速、可靠的方法。它的快速算法对数据量巨大的遥感图像更具实用价值。②从提高TM6遥感影像空间分辨率的仿真结果来看,无论计算效率还是遥感影像的融合效果,GA-BP算法都优于BP算法。③GA-BP算法既保留了TM6遥感影像的基本灰度分布信息,同时也提高了其空间分辨率,可以有效地运用到提高遥感影像空间分辨率的过程中。     

基于波谱匹配的Hyperion数据大气校正方法对比研究

遥感影像数据的大气校正是高光谱遥感地空对比、信息提取的前提和关键,如何根据不同数据、不同研究区、不同研究目的选择合适的大气校正方法是高光谱遥感应用研究的重点和难点。针对EO\|1卫星Hyperion高光谱遥感数据特点和研究区地形环境特征,分别选择线性回归经验模型、基于MODTRAN4模型的FLAASH和基于DEM数据的ACORN\|3模型不同大气校正方法对研究区Hyperion数据进行大气校正。从波谱匹配、识别的目的出发,通过计算不同方法校正后影像像元的波谱曲线与实测地面波谱曲线的匹配程度分析不同大气校正方法的校正效果。     

融合边缘特征和对抗网络的遥感影像超分辨重建方法

遥感影像中含有较为丰富的高频信息，但受硬件设备限制和外界环境干扰，高频信息边缘部分较为模糊，影响遥感影像的空间分辨率质量。针对这一问题提出一种基于边缘检测和密集残差的遥感影像超分辨率重建方法。该方法利用边缘检测算子提取影像边缘细节特征，采用密集残差模块代残差网络复用底层特征，并将每一层的特征图进行加权融合，最后通过生成器完成遥感影像的超分辨率重构，实现遥感影像分辨率质量的提高。实验结果表明，该算法指标优于传统的双三次插值、SRCNN、VDSR和SRGAN,为遥感影像重建提供新的解决方法与技术思路。     

Mapping spatial variations of iron oxide by-product minerals from EO-1 Hyperion

This study aimed to map mine waste piles and iron oxide by-product minerals from an Earth Observing 1 (EO-1) Hyperion data set that covers an abandoned mine in southwest Spain. This was achieved by a procedure involving data pre-processing, atmospheric calibration, data post-processing, and image classification.

In several steps, the noise and artefacts in the spectral and spatial domains of the EO-1 Hyperion data set were removed. These steps include the following: (1) angular shift, which was used to translate time sequential data into a spatial domain; (2) along-track de-striping to remove the vertical stripes from the data set; and (3) reducing the cross-track low-frequency spectral effect (smile). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm in combination with the radiance transfer code MODTRAN4 was applied for quantification and removal of the atmospheric affect and retrieval of the surface reflectance. The data set was post-processed (filtering, spectral polishing) in order to remove the negative values and noise that were produced as the a result of de-striping and atmospheric calibration. The Mahalanobis distance algorithm is used to differentiate the area covered by mine piles from other main land-use classes. The spatial variations of iron oxide and carbonate minerals within the mine area were mapped using the Spectral Feature Fitting (SFF) algorithm.

The pre-processing of the data and atmospheric correction were vital and played a major role on the quality of the final output. The results indicate that the vertical stripes can be removed rather well by the local algorithm compared to the global method and that the FLAASH algorithm for atmospheric correction produces better results than the empirical line algorithm. The results also showed that the method developed for correcting angular shifts has the advantage of keeping the original pixel values since it does not require re-sampling.

The classification results showed that the mine waste deposits can be easily mapped using available standard algorithms such as Mahalanobis Distance. The results obtained from the SFF method suggest that there is an abundance of different minerals such as alunite, copiapite, ferrihydrite, goethite, jarosite, and gypsum within the mine area. From a total number of 754 pixels that cover the mine area, 43 pixels were classified as sulphide and carbonate minerals and 711 pixels remained unclassified, showing no abundance of any dominant mineral within the area presented by these pixels.     

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.     

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.     

高程和大气模式对FLAASH模型校正结果的影响

为探讨FLAASH大气校正模型对高程和大气模式参数的敏感性,以中纬度夏季和亚极地夏季大气模式过渡区的两景Landsat8 OLI遥感影像为基础,从高程、大气模式及其综合作用进行了对比分析。实验结果表明:①单景影像而言,FLAASH模型与高程和两种大气模式敏感性不强,各波段反射率平均值相差在0.21%以内;②在相邻两景影像重叠区,FLAASH模型与高程和大气模式敏感性明显加强,可见光波段反射率均值相差在0.5%~1.29%之间;③FLAASH模型能有效去除Landsat8影像的大气影响,并使图像信息增强,大气校正的作用效果在短波波段更加明显。
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Mapping wild pear trees (Pyrus bourgaeana) in Mediterranean forest using high-resolution QuickBird satellite imagery

Recent advances in spatial and spectral resolution of satellite imagery as well as in processing techniques are opening new possibilities of fine-scale vegetation analysis with interesting applications in natural resource management. Here we present the main results of a study carried out in Sierra Morena, Cordoba (southern Spain), aimed at assessing the potential of remote-sensing techniques to discriminate and map individual wild pear trees (Pyrus bourgaeana) in Mediterranean open woodland dominated by Quercus ilex. We used high spatial resolution (2.4 m multispectral/0.6 m panchromatic) QuickBird satellite imagery obtained during the summer of 2008. Given the size and features of wild pear tree crowns, we applied an atmospheric correction method, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and six different fusion ‘pan-sharpening’ methods (wavelet ‘à trous’ weighted transform, colour normalized (CN), Gram–Schmidt (GS), hue–saturation–intensity (HSI) colour transformation, multidirection–multiresolution (MDMR), and principal component (PC)), to determine which procedure provides the best results. Finally, we assessed the potential of supervised classification techniques (maximum likelihood) to discriminate and map individual wild pear trees scattered over the Mediterranean open woodland.     

Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements

A radiative transfer approach to the problem of atmospheric correction of satellite images in the solar spectral range is presented which includes all multiple scattering processes without any approximation. The numerical solution is accepted as satisfying, if the numerical accuracy is better than I per cent. This means that the accuracy of the atmospheric correction depends almost exclusively on the quality of the auxiliary data on the atmospheric state and the surface reflection indicatrix. Byextensivemodel calculations these parameter driven error bounds have been quantified. Thus the calculation results in a corrected albedo image with specified error bounds. This seems to be the first algorithm available for atmospheric correction of real imagery data which relies on a numerical exact treatment of multiple scattering. The program EXACT (EXact Atmospheric Correction Technique) has so far been used with Landsat Thematic Mapper (TM), NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and also with airborne Daedalus ATM images. The algorithm has been validated by comparison of satellite data to ground measurements and between different sensors. Errors of the derived albedos were found to remain below 0·01 for visible and near-infrared sensor channels of this set of radiometers.     

Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products

In this paper we analyze the differences obtained in the atmospheric correction of optical imagery covering bands located in the Visible and Near Infra-Red (VNIR), Short-Wave Infra-Red (SWIR) and Themal-Infrared (TIR) spectral regions when atmospheric profiles extracted from different sources are used. In particular, three sensors were used, Compact High Resolution Imaging Spectrometer (CHRIS), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) and Landsat5 Thematic Mapper (TM), whereas four atmospheric profiles sources were considered: i) local soundings launched near the sensor overpass time, ii) Moderate Resolution Radiometer (MODIS) atmospheric profiles product (MOD07), iii) Atmospheric Correction Parameter Calculator (ACPC) generated by the National Center for Environmental Prediction (NCEP) and iv) Modified Atmospheric Profiles from Reanalysis Information (MAPRI), which includes data from NCEP and National Center of Atmospheric Research (NCAR) Reanalysis project but interpolated to 34 atmospheric levels and resampled to 0.5° × 0.5°. MODIS aerosol product (MOD04) was also used to extract Aerosol Optical Thickness (AOT) values at 550 nm. Analysis was performed for three test dates (12th July 2003, 18th July 2004 and 13th July 2005) over an agricultural area in Spain. Results showed that air temperature vertical profiles were similar for the four sources, whereas dew point temperature profiles showed significant differences at some particular levels. Atmospheric profiles were used as input to MODTRAN4 radiative transfer code in order to compute atmospheric parameters involved in atmospheric correction, with the aim of retrieving surface reflectances in the case of VNIR and SWIR regions, and Land Surface Temperature (LST) in the case of the TIR region. For the VNIR and SWIR region, significant differences depending on the atmospheric profile used were not found, particularly in the Visible region in which the AOT content is the main parameter involved in the atmospheric correction. In the case of TIR, differences depending on the atmospheric profile used were appreciable, since in this case the main parameter involved in the atmospheric correction is the water vapor content, which depends on the vertical profile. In terms of LST retrieval from ASTER data (2004 test case), all profiles provided satisfactory results compared to the ones obtained when using a local sounding, with errors of 0.3 K for ACPC and MAPRI cases and 0.7 K for MOD07. When retrieving LST from TM data (2005 test case), errors for MOD07 and MAPRI were 0.6 and 0.9 K respectively, whereas ACPC provided an error of 2 K. The results presented in this paper show that the different atmospheric profile sources are useful for accurate atmospheric correction when local soundings are not available. In particular, MOD07 product provides atmospheric information at the highest spatial resolution, 5 km, although its use is limited from 2000 to present, whereas MAPRI provides historical information from 1970 to present, but at lower spatial resolution.     

HJ-1A高光谱数据高效大气校正及应用潜力初探

环境与灾害监测预报小卫星于2009年3月30日开始正式交付使用,A星上搭载了我国自主研制的空间调制型干涉高光谱成像仪（HSI）,作为一种新型传感器,HSI数据的应用在我国还处于探索阶段。要充分发挥超光谱数据优势、进行有效的遥感应用,首先需要消除遥感成像过程中的大气影响,获得不同波段的地物真实反射辐射信息。通过使用FLAASH大气辐射传输模型对HSI数据进行大气校正,并与表观反射率进行对比分析,证明了校正后获得的地表光谱反射率的有效性。同时基于校正后得到的光谱反射率图像,进行改良型土壤调整植被指数（MSAVI）与叶面积指数（LAI）的反演,初步展现了HSI数据的实际应用效果。     

Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals

ASTER reflectance spectra from Cuprite, Nevada, and Mountain Pass, California, were compared to spectra of field samples and to ASTER-resampled AVIRIS reflectance data to determine spectral accuracy and spectroscopic mapping potential of two new ASTER SWIR reflectance datasets: RefL1b and AST_07XT. RefL1b is a new reflectance dataset produced for this study using ASTER Level 1B data, crosstalk correction, radiance correction factors, and concurrently acquired level 2 MODIS water vapor data. The AST_07XT data product, available from EDC and ERSDAC, incorporates crosstalk correction and non-concurrently acquired MODIS water vapor data for atmospheric correction. Spectral accuracy was determined using difference values which were compiled from ASTER band 5/6 and 9/8 ratios of AST_07XT or RefL1b data subtracted from similar ratios calculated for field sample and AVIRIS reflectance data. In addition, Spectral Analyst, a statistical program that utilizes a Spectral Feature Fitting algorithm, was used to quantitatively assess spectral accuracy of AST_07XT and RefL1b data.Spectral Analyst matched more minerals correctly and had higher scores for the RefL1b data than for AST_07XT data. The radiance correction factors used in the RefL1b data corrected a low band 5 reflectance anomaly observed in the AST_07XT and AST_07 data but also produced anomalously high band 5 reflectance in RefL1b spectra with strong band 5 absorption for minerals, such as alunite. Thus, the band 5 anomaly seen in the RefL1b data cannot be corrected using additional gain adjustments. In addition, the use of concurrent MODIS water vapor data in the atmospheric correction of the RefL1b data produced datasets that had lower band 9 reflectance anomalies than the AST_07XT data. Although assessment of spectral data suggests that RefL1b data are more consistent and spectrally more correct than AST_07XT data, the Spectral Analyst results indicate that spectral discrimination between some minerals, such as alunite and kaolinite, are still not possible unless additional spectral calibration using site specific spectral data are performed.     

基于高程分层方法的HJ-1B CCD2影像大气校正

大气校正是从环境一号B星(HJ\|1B)CCD2多光谱卫星数据中精确提取地面定量信息的关键一步。采用一种基于高程分层和改进的浓密植被算法,即将整个研究区按照高程间隔0.1 km划分为17个子区域,在不同高程带内利用红波段与短波红外波段(1.6 μm)的线性关系估计出红波段的反射率,然后利用估计的红波段反射率与其表观反射率差值的均值,结合6S辐射传输模型模拟计算得到0.55 μm处的气溶胶光学厚度,从而实现各个波段大气校正。比较分析校正前后成熟林地、水体和裸土的光谱反射率和标准反射率,结果表明:该方法对HJ-1B CCD2数据大气校正可取得较好的效果。