“环境与灾害监测预报小卫星”高光谱成像仪

重点介绍作者为“环境与灾害检测预报小卫星”研制的空间调制型Fourier变换高光谱成像仪,介绍了该类仪器的基本原理和“环境与灾害检测预报小卫星”高光谱成像仪总体方案,详细分析了仪器总体技术参数设计方法以及光学、机械、电子、定标和数据复原处理等环节的关键技术,给出了仪器在轨获得的初步数据结果。     

HJ-1A/B星CCD多光谱遥感数据特征评价及应用研究

我国现有的风云系列卫星、资源卫星及海洋卫星都可实现多种性能的对地观测,在灾害与环境监测方面发挥了很好的作用。然而,面对复杂多样的自然灾害及环境问题,这些卫星却都难以完全满足灾害和环境监测与预报所需的空间分辨率、时间分辨率和光谱分辨率以及全天候的要求。主要对环境与灾害监测预报小卫星A、B(HJ-1A/B)星多光谱遥感数据进行了质量评价,提取数据特征,以便为该数据的推广应用提供参考。     

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。     

基于MODIS多通道合成的江淮暴雨云系分析方法

卫星多通道合成能直观反映出卫星云图上的一些特性,通过采用经过预处理后的MODIS多通道数据,利用“白天自然色”、“白天微物理”、“白天太阳”和“空气团”4种多通道组合,结合实况降水分布和地面气象观测站点资料,以2010年7月13日江淮流域的一次特大暴雨过程为例,定性分析暴雨云系微观物理性质,推断云粒子大小和相态等,并对比高时空分辨率的局地分析预报系统(LAPS)中尺度物理量场以及不同研究个例的多通道合成图分析。结果表明：卫星多通道RGB合成图能以色彩的形式有针对性地突出对流系统、云粒子微观物理性质等属性,具有一定的精确度和普适性,有利于暴雨等中尺度强对流天气的监测。     

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。     

一种针对环境一号卫星A星高光谱与CCD数据融合的方法

利用我国自主研制的环境一号小卫星A星上搭载的CCD相机与高光谱成像仪(HSI),以洪河国家级湿地为研究区,进行融合实验。先分析了两种载荷的特点,然后根据载荷特点,设计并评价了两种载荷数据融合的方法。结果表明,由于A星CCD相机与高光谱成像仪成像条件相似,可以对两个载荷的数据进行融合,得到具备较高分辨率与光谱分辨率的数据,为后续高精度分类制图、目标识别、信息提取等应用提供高质量数据源。     

POLDER 多角度、多通道偏振探测器对地遥感观测研究进展

由法国空间研究中心(CNES) 于2004 年12 月18 日发射的PARASOL (Polarization &Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) 卫星, 是法国和美国合作的“卫星列车”(“A-Train ”) 计划中的一员, 上面主要搭载了POLDER(Polarization and Directionality of the Earth’s Reflectances) 仪器, 可以通过全球观测, 从空间收集地气系统反射太阳辐射的偏振性和方向性数据。从这些数据, 我们可以获得地表、大气气溶胶及云的多种特性, 这些信息是改进气候数值模式所必需的。主要以POLDER 仪器为例, 就多光谱、多角度和多偏振联合遥感观测地气系统方面的进展进行综述。     

“北京一号”小卫星数据在天津市土地利用动态变化监测中的应用

应用“北京一号”小卫星CCD 数据进行天津市2000～ 2005 年土地利用动态变化监测, 研究该数据在土地利用研究领域所具有的实用性和效果。结果表明,“北京一号”小卫星遥感影像, 光谱信息丰富, 纹理结构清晰, 整体判读情况良好, 能够满足中等比例尺成图要求, 在土地利用研究领域具有实用性, 可以在土地利用ö土地覆盖的研究中广泛使用, 成为遥感数据更新的一种新数据源。动态监测显示, 2000～ 2005 年天津市不同土地利用类型之间相互转化明显。整体表现出了城镇用地通过占用近郊的耕地和其它类型用地而迅速扩展、城镇化过程显著以及不同土地利用类型之间转化明显等基本特征。     

黑河综合遥感联合试验的数据管理与共享

“黑河综合遥感联合试验”(Watershed Allied Telemetry Experimental Research,WATER)是在黑河流域开展的以水循环及与之密切联系的生态过程为主要研究对象的大型航空、卫星遥感与地面同步观测科学试验。为了对试验产出和处理后的数据进行统一的管理和共享,促进试验成果在更大范围内的应用,“黑河综合遥感联合试验”建立了数据管理系统,并在中国科学院寒区旱区环境与工程研究所和北京师范大学成立了两个数据服务点负责数据的管理和分发。“黑河综合遥感联合试验”的数据管理系统是一个以元数据为核心的数据管理和发布系统,在数据管理方面介绍了元数据、数据文档和数据实体的组织与管理,在系统功能方面介绍了数据导航和数据可视化。“黑河综合遥感联合试验”目前已发布的原始数据共3.2 TB,预处理的数据2.5 TB,以粗粒度的形式组织了265个数据集,265条元数据与数据报告一起对数据集进行说明。     

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。     

基于行频变化的航空高光谱成像仪相对辐射校正方法研究

对于核心部件为线阵CCD阵列的航空高光谱成像仪而言,积分时间因随飞行过程中成像行频动态变化而变化,导致影像中各行数据记录的地物灰度值不具备可比性,更使得相邻航带可能出现明显色差。为此,发展了一种基于行频变化的相对辐射校正方法,在传统的相对辐射校正模型中引入了行频的影响,实现了反映行频动态变化的航空高光谱数据辐射校正三维曲面模型。通过无人机载高光谱成像仪的航空飞行试验,利用所获取的验证场的图像进行相对辐射校正处理,通过相邻航带间图像处理前后色差比较、验证场布设均匀靶标的定量化。     

An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications

An AOTF (Acousto-Optic Tunable Filter)-based spectral imager was developed for hyperspectral measurement of plant reflectance in the field. A hyperspectral image cube for the spectral region between 450-900 nm could be acquired at 3 to 5 nm resolution intervals within a few seconds. The system was light and compact, and both the spectral wavelengths and intervals were programmable with PC control. Wavelengths could be tuned rapidly, either sequentially or randomly. The hyperspectral image cube for rice canopies obtained by the system showed its potential in the estimation of leaf nitrogen and chlorophyll concentrations. The AOTF-based hyperspectral system would have great potential for further investigations in remote sensing of biochemical and ecophysiological plant variables.     

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multi-core central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable real-time anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.     

基于光谱特征的城市人工地物分级分类方法研究

１９９７年６月利用ＰＨＩ推帚式成像光谱仪在北京市沙河镇进行了飞行实验,其主要目的是成像光谱技术应用于城市用地和建筑和分类中关键技术研究。对所获取的１５个波段的可见光－近红外数据进行了分析与处理,试图通过对地面覆盖物质的光谱响应特征的分析来进行城市人工目标的识别与分类。由于城市地物的光谱特征异常复杂,难于应用一般的模式分类算法分离出所有类型,本文在对地面覆盖类型光谱响应分析的基础上,采用分层复合分类     

遥感影像光谱信息处理的EXCEL实践教学

遥感影像数据的光谱信息分析与处理是目前遥感应用的热点之一。通过多年的遥感教学实践,依据PHI（Pushbroom Hyperspectral Imager）航空高光谱影像像元光谱维矢量数据,总结了基于EXCEL软件的高光谱影像地物光谱构建、光谱特征及其相关性分析、光谱微分计算、光谱向量相似性度量等光谱分析与信息识别的教学方法,以使学生在学习专业知识过程中增加实际工作经验与科研能力。     

基于光谱特征的城市人工地物分级分类方法研究

１９９７ 年６ 月利用ＰＨＩ推帚式成像光谱仪在北京市沙河镇进行了飞行实验,其主要目的是成像光谱技术应用于城市用地和建筑物分类中关键技术研究。对所获取的１５ 个波段的可见光－ 近红外数据进行了分析与处理,试图通过对地面覆盖物质的光谱响应特征的分析来进行城市人工目标的识别与分类。由于城市地物的光谱特征异常复杂,难于应用一般的模式分类算法分离出所有的类型,本文在对地面覆盖类型光谱响应分析的基础上,采用分层复合分类的方法,逐步将不同的地物类型分离开来,实验证明是非常有效的     

基于颜色自适应字典的双相机光谱系统重建算法

通过将快照编码孔径光谱成像和普通RGB彩色成像结合,双相机光谱成像系统能够高效地获取场景的光谱信息,具有广阔的应用前景.如何高质量地从压缩采样中重建高光谱图像是该系统需要解决的重要问题.根据高光谱图像与彩色图像在空间结构和光谱响应上的相关性,本文了提出一种基于颜色自适应字典的重建算法,用以提高双相机光谱成像系统的重建质量.首先,利用RGB观测分别训练三通道非负字典.然后,以彩色相机的光谱响应曲线为指导,为每一个谱带选择光谱相关性最大的字典.最后,完成高光谱图像的稀疏重建.高光谱数据库和遥感数据库的仿真结果均表明,本文提出的算法能够大幅度提升双相机光谱成像系统的重建质量.     

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。     

Commodity cluster-based parallel processing of hyperspectral imagery

The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.     

Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations

Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. In particular, NASA is continuously gathering very high-dimensional imagery data from the surface of the Earth with hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) satellite platform. The development of efficient techniques for extracting scientific understanding from the massive amount of collected data is critical for space-based Earth science and planetary exploration. In particular, many hyperspectral imaging applications demand real time or near real-time performance. Examples include homeland security/defense, environmental modeling and assessment, wild-land fire tracking, biological threat detection, and monitoring of oil spills and other types of chemical contamination. Only a few parallel processing strategies for hyperspectral imagery are currently available, and most of them assume homogeneity in the underlying computing platform. In turn, heterogeneous networks of workstations (NOWs) have rapidly become a very promising computing solution which is expected to play a major role in the design of high-performance systems for many on-going and planned remote sensing missions. In order to address the need for cost-effective parallel solutions in this fast growing and emerging research area, this paper develops several highly innovative parallel algorithms for unsupervised information extraction and mining from hyperspectral image data sets, which have been specifically designed to be run in heterogeneous NOWs. The considered approaches fall into three highly representative categories: clustering, classification and spectral mixture analysis. Analytical and experimental results are presented in the context of realistic applications (based on hyperspectral data sets from the AVIRIS data repository) using several homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and the University of Maryland.