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。     

Hyperion数据产品及预处理研究

高光谱遥感是近年来发展起来的一种全新的遥感技术,其在对地成像的同时能够获取地物波谱特征。鉴于河北省高光谱遥感工作目前处于探索阶段,本文以河北省北部丰宁地区一景星载高光谱遥感数据（Hyperion）为例,详细介绍了星载高光谱遥感数据产品的组成、产品命名规则、数据特点及预处理方法,为高光谱遥感技术在地质找矿、环境监测、精细农业等领域的应用提供技术支撑。     

Hyperion数据玉米叶绿素含量制图

采用星地同步观测方法,对Hyperion影像进行预处理并提取玉米专题信息,计算与遥感影像同步获取的玉米地面实测光谱及其一阶微分形式,作物光谱指数参量与叶绿素含量之间的相关性。结果表明:作物叶绿素含量预测指数TCARI/OSAVI与叶绿素a和叶绿素b的相关性最好,R²分别为0.5694和0.5313。采用其与叶绿素含量进行回归分析,建立叶绿素反演模型。将回归结果应用到提取的玉米区域,得出叶绿素a和叶绿素b含量的空间分布图,直观显示玉米的长势状况,为农业估产和植被长势监测提供重要的数据源。     

基于高光谱数据的河北滦平地区岩性信息提取方法试验研究

Hyperion高光谱数据在蚀变矿物填图方面已广泛应用,而在岩性信息提取方面应用较少.本文以河北滦平地区的Hyperion高光谱数据为数据源,通过对预处理后的Hyperion数据进行最小噪声分离变换(MNF),计算纯净像元指数(PPI),N维光谱空间特征端元采集,结合野外光谱采集进行光谱分析识别端元,最后用光谱角(SAM),光谱信息散度(SID)和二值编码(BE)3种方法进行岩性分类.与已知地质图叠加,通过分类图中不同岩石类型颜色边界与地质图岩性界线吻合程度以及与研究区地质解译分类图的对比来比较3种分类方法.结果表明,SAM方法的分类吻合程度高于其他两种方法,SAM方法是一种有效的高光谱遥感岩性分类方法.     

HJ-1A高光谱数据预处理方法研究

高光谱遥感数据处理与应用已成为目前遥感应用研究领域的热点和难点之一。介绍了环境与减灾监测预报小卫星中高光谱遥感数据产品命名规则和内容,以及产品数据的主要特点,通过对HSI数据辐射定标、Flaash大气校正和几何精校正等数据预处理方法,得出Flaash大气校正后的HSI数据,提高了高光谱数据的质量,地物光谱曲线更加符合实际情况,为下一步定量遥感研究奠定了基础。     

基于高光谱遥感影像的大气纠正:用AVIRIS数据评价大气纠正模块FLAASH

高光谱遥感影像由于集中了高光谱分辨率和高空间分辨率的优点,在对地观测中具有不可替代的优势。实际应用当中,往往需要从遥感影像获取地物的地表反射率信息,这就要求首先从影像中去除大气的影响,即进行大气纠正及补偿。目前,对遥感影像进行大气纠正的算法有很多,详细介绍了基于遥感影像自身信息的大气纠正模块FLAASH(Fast Line of Sight Atmospheric Analysis f Spectral Hypercubes)所涉及的算法,并利用该模块对AVIRIS航空遥感影像进行了大气纠正, 对不同的结果进行了分析对比,从而对该算法进行了初步的评价。     

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。     

高光谱数据反演大气水汽研究

利用Hyperion高光谱数据,基于MODTRAN辐射传输模型进行模拟,分析表明943nm和953nm波段分别对干燥和湿润大气最敏感,是反演水汽的合适通道。提出针对Hyperion数据DCIBR方法反演大气总水汽含量,可以推广到环境与灾害监测预报小卫星星座超光谱数据的大气水汽反演。误差分析表明,大气散射作用以及地表反射率非线性变化,对DCIBR方法计算精度的影响比CIBR算法要小,大气模式引起的反演误差小于2%,通道的光谱特性是影响反演精度的重要因素。     

高光谱遥感分类方法研究

高光谱遥感是近二十年发展起来的遥感前沿技术。本文探讨了高光谱遥感分类方法,比较分析各种方法与传统方法的优缺点。     

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。     

EO-1 Hyperion数据的预处理、特征提取和岩性填图研究

EO-1 Hyperion传感器是第一个可以获取可见光与近红外以及短波红外波长范围光谱信息的星载高光谱传感器。本文以美国最早的金矿采矿区之一,加利福尼亚州东南巧克力山的Rainbow金矿区作为研究案例,探讨了Hyperion数据的预处理方法,专题信息提取与填图,评估了Hyperion高光谱数据在识别与金矿有关的岩性类型的应用价值。结果表明,本文所提出的Hyperion数据预处理方法是有效的,MNF方法能有效用于Hyperion数据维数的降低和数据冗余的去除以及分类特征的提取。最大似然分类器能够有效地从Hyperion高光谱数据中提取与金矿相关的重要岩体信息,所得到的岩性单元与地质图上对应的岩性分布具有很好的一致性。岩体分类的总精度为86%。该研究表明,Hyperion高光谱数据能够很好识别有细微光谱差别的岩性,因而在地质学研究与找矿领域有着良好的应用前景。     

Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data

Hyperspectral data acquired by the Hyperion instrument, on board the Earth Observing-1 (EO-1) satellite, were evaluated for the discrimination of five important Brazilian sugarcane varieties (RB72-454, SP80-1816, SP80-1842, SP81-3250, and SP87-365). The radiance values were converted into surface reflectance images by a MODTRAN4-based technique. To discriminate varieties with similar reflectance values, multiple discriminant analysis (MDA) was applied over the data. To obtain an adequate discriminant function, a stepwise method was used to select the best variables among surface reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll content, leaf water, and lignin-cellulose. Results showed that the five Brazilian sugarcane varieties were discriminated using EO-1 Hyperion data. Differences in canopy architecture affected sunlight penetration and reflectance, resulting in a higher reflectance for planophile (e.g., SP81-3250) than erectophile (e.g., SP80-1842) sugarcane plants. The variety SP80-1842 presented lower reflectance values, deeper lignin-cellulose absorption bands at 2103 nm and 2304 nm, shallower leaf liquid water absorption bands at 983 nm and 1205 nm, and lower leaf liquid water content than the other sugarcane varieties. To discriminate this cultivar, a single near-infrared (NIR) band threshold was used. To discriminate the other four sugarcane varieties with similar reflectance values, MDA was used producing a classification accuracy of 87.5% for a hold-out set of pixels. The comparison between the ground truth data and the MDA-derived classification image confirmed the model' capacity to differentiate the varieties accurately. The best results were obtained for the cultivar SP87-365 that presented the lowest spectral variability in the discriminant space. Some misclassified areas were associated with the cultivars SP80-1816 and SP81-3250 that showed the highest spectral variability.     

Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1

In this paper, we present a new way of detecting and monitoring flooding through the Autonomous Sciencecraft Experiment (ASE) [Chien, S. T., Debban, C., Yen, R., Sherwood, R. Castano, B., & Cichy, A. G. et al. (2001). ASC Science Study Report, available from http://ASE.jpl.nasa.gov], which is part of the Space Technology 6 effort under NASA's New Millennium Program. Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the ASE flight software have demonstrated the ability of several science algorithms to successfully classify key features including flood-induced changes, in hyperspectral images captured by the EO-1 Hyperion instrument. Furthermore, onboard science analysis on the classified images has been performed, and then used to modify an operational plan without interaction from the ground (Sherwood, R., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A., et al. (2004). Preliminary results of the autonomous sciencecraft experiment. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT). These algorithms are used to downlink science data only when change occurs, and to detect features of scientific interests such as flooding, volcanic eruptions, and the formation and breakup of sea ice. The purpose of this paper is to demonstrate the success of ASE and its implications on detecting, mapping, and monitoring transient processes such as flooding autonomously from space. Mapping of water inundation and its change through time is part of our focus in studying transient processes from space.In 2004, hyperspectral data were acquired from the Hyperion instrument for target areas around the world that have a high potential for flooding to develop and test floodwater classifiers. In addition, classifier thresholds were determined from both normal flows and possible flood conditions. The paper introduces the development, testing, and success of the ASE software in detecting and reacting to flooding in near real-time. ASE is now operational and flight-tested, and, thus, ready to use for space-borne reconnaissance. Successful demonstration of the floodwater classifiers includes the capture of a rare flooding event of the Australian Diamantina River during ground testing in February 2004, and the detection of flood-related changes along the Brahmaputra River in Bangladesh and the Yukon River in Alaska during onboard testing on EO-1 in 2005. Both of these detections led to triggered responses onboard the spacecraft, which included acquiring additional Hyperion scenes. These results pave the way for future smart reconnaissance missions of transient processes on Earth and beyond. It is hoped that ASE will become a default in future missions to increase the science return by introducing spacecraft autonomy for detection and monitoring of science events, which otherwise would be discovered too late or altogether missed.     

基于贝叶斯树算法的高光谱遥感数据分类

文章针对高光谱波段数众多、信息冗余量大的特点,首先对高光谱曲线进行光谱特征参数提取,然后再选择合适的吸收峰波段作为输入向量,在VS2008平台上实现了采用贝叶斯树（NBTree）算法对铀矿床高光谱数据进行分类。     

高光谱遥感数据挖掘若干基本问题的研究

面向高光谱遥感信息的特点，分析了高光谱遥感数据挖掘的形成和作用，在构建其框架体系与处理流程的基础上。探讨了可以发现的知识类型和典型的挖掘模式，并分析了一些主要挖掘算法和关键技术。最后对高光谱遥感数据挖掘潜在的应用方向进行了探讨。     

Spatial-Spectral Preprocessing based on Nonnegative Matrix Factorization to Unmix Hyperspectral Data

Non-negative Matrix Factorization (NMF)method of blind spectral unmixing can obtain the spectrum and abundance of the endmember by synchronous optimization,without supervising the selection of endmember.Therefore,NMF has been developed rapidly in the application of hyperspectral unmixing.However,traditional blind spectral unmixing NMF method tends to fall into the local optimum and it is difficult to obtain a stable optimal solution.In this paper,we propose an improved Non-negative Matrix Factorization (NMF)method based on Spatial\|Spectal Preprocessing for spectral unmixing of hyperspectral data (SSPP-NMF).First,the SSPP algorithm is used to combine spatial and spectral information to select reasonable and effective dataset.Then,the NMF algorithm is used to unmix this dataset to obtain the final optimized endmember spectrum.Finally,the Non\|Negative Least Squares (NNLS)method is used to obtain the final abundance of the whole study area.The validity and applicability of the proposed method were analyzed based on a set of synthetic hyperspectral data and real hyperspectral images;and then the results were compared with that from three algorithms including the existing NMF algorithm,MVC\|NMF algorithm and ATGP-NMF algorithm.Results show that compared with ATGP-NMF and MVC-NMF,the SSPP algorithm can effectively suppress the influence of noise,significantly improve the performance of the NMF method of blind spectral unmixing algorithm.     

森林树种高光谱波段的选择

高光谱是遥感技术发展的一个重要方向，也是地物识别的重要手段。本研究利用地物光谱仪对杉木、雪松、小叶樟树和桂花树4个树种进行高光谱数据测量，探索不同树种在不同波段上的识别能力。研究采用了逐步判别分析法和分层聚类法对实验数据进行数据分析。结果表明：逐步判别分析法选择的波段主要位于红、绿、蓝、和近红外区；分层聚类法选择的波段除了红、绿、蓝、和近红外波段外，还增加了蓝-绿边缘、绿-红边缘和红边区的波段。所选择的波段比原始波段在树种识别时具有更高的精度，最高识别精度达96．77％；边缘区波段对树种的识别有重要作用；用对数-微分变换处理较其他方法处理对树种识别有更好的效果。     

基于主分量分析的高光谱遥感数据噪声消除方法

高光谱遥感数据是一种超维数据,噪声随机地存在于其多个通道图像上,而某些特征谱段图像的信噪比在很大程度上影响着具体的光谱特性分析的准确性,高信噪比意味着地物探测或识别的高准确性;提出了一种新颖的基于小波-PCA分析的高光谱遥感数据噪声消除方法,首先对高光谱各波段图像进行二维小波分解,然后将各小波子图像进行主分量分析,根据噪声特性自适应选取主分量进行PCA重构;得到去噪后小波图像,最后进行小波重构。通过对OMIS图像的实验结果表明,该方法不但可以有效地消除高光谱遥感数据中的噪声,改善图像的信噪比,还能比较完整地保留原有高光谱数据的空间和光谱特性,且性能上要优于PCA方法和小波分析方法。     

机载高光谱数据提取冬小麦冠层叶绿素含量的模型分析及验证

遥感提取叶绿素含量的方法是精准农业的重要研究方向之一,但是如何用冠层光谱数据有效地提取叶绿素含量仍然是一个难点。本文用光谱指数TCARI和OSAVI的组合建立提取冬小麦冠层叶绿素含量的关系式,并使用实验田获取的冬小麦冠层光谱以及与之同步的机载高光谱传感器OMIS数据进行了验证。通过误差分析讨论了该方法用于遥感高光谱数据时需要注意的问题,表明大气校正的精度,传感器的信噪比以及波段中心的漂移是模型反演精度的主要制约因素。