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.     

基于Hyperion数据的铜矿信息直接和间接提取方法研究

针对不同的地表覆盖条件,研究基于Hyperion星载高光谱数据的砂页岩型铜矿信息提取方法。首先对Hyperion L1级数据进行波段筛选、坏线修复、大气纠正和几何纠正等预处理,然后分别针对不同的植被覆盖情况使用不同的信息提取方法。在岩石裸露区,直接使用光谱角制图法;在植被覆盖区,使用铜金属离子的积累导致的植被生理异常作为间接标志来识别铜矿信息,生理异常使用高光谱植被指数来计算。结果表明,综合使用这两种方法的互补信息能够便于提取复杂地表覆盖情况下的铜矿信息。     

基于SAM-SCP组合方法的热红外高光谱影像岩性分类

为了改善传统岩性分类方法中对光谱局部细节特征分析不足的问题，在光谱角分类模型的基础上，有效地融合地物的光谱特征参量，提出了一种新型的光谱角和光谱特征参量（Spectral Angle Mapper-Spectral Characteristic Parameters，SAM-SCP）组合的分类方法。该方法既体现了光谱的形状特征，又能够充分利用光谱的细节特征，解决由于岩性光谱曲线形状相似而识别效果差的问题，提高了分类精度。应用SAM-SCP分别对模拟热红外高光谱数据与真实热红外高光谱数据进行实验，并在SCP的设定过程中，调整主要谷、次要谷所占的权重以获得最佳的分类效果，最终的结果证明：SAM-SCP能对热红外高光谱影像进行有效的岩性分类，分类结果优于传统分类方法。     

高光谱遥感数据光谱特征提取算法与分类研究*

针对高光谱数据的特点,探讨了高光谱数据特征提取的若干算法,重点研究了导数光谱和光谱编码技术,并从地物光谱曲线中提取了其光谱吸收特征.对同类曲线特征求交得到识别地物的有效特征;对不同类曲线特征求交得到区分不同类地物的有效特征.最后基于提取的特征建立了地物识别决策树,从而达到快速识别分类地物的目的,能够实现依据地物光谱特征的地物识别与分类.     

ELM与SVM在高光谱遥感图像监督分类中的比较研究

在高光谱遥感图像监督分类过程中加入空间特征信息,可有效提高分类的速度与精度。将空间信息提取方法分水岭法与极限学习机(ELM)和支持向量机(SVM)相结合,对两种分类方法加入空间特征信息前后的分类结果进行时间与精度的综合评价与比较分析。以意大利帕维亚大学(PaviaU)ROSIS和博茨瓦纳(Botswana)奥卡瓦纳三角洲Hyperion高光谱遥感数据进行试验,首先对原始图像数据进行预处理,对不同地物类别选取适当的训练样本作为分类的参考区域,然后对各类别的光谱特征进行分析,并分别运用两种分类方法对数据集进行分类实验;之后将光谱特征与空间特征结合对数据进行分类试验。实验结果表明:在分类时间及精度方面,极限学习机(ELM)均优于支持向量机(SVM);在分类过程中引入空间特征信息,可有效提高分类精度。     

基于混合像元分解的高光谱影像柑橘识别方法

为及时准确地监测柑橘种植信息,以江西省会昌县作为研究区,采用EO-1 Hyperion高光谱影像作为数据源,构建了基于混合像元分解的高光谱影像柑橘识别方法。首先,针对EO-1 Hyperion高光谱影像提供了242个波段,光谱范围广的特点,在波段选择、大气校正等预处理的基础上,提取研究区典型地物端元光谱曲线;然后,利用全约束线性光谱混合模型进行混合像元分解,提取出柑橘端元的丰度值,并通过对照高分遥感影像,构建柑橘端元丰度与柑橘实际种植的对应的关系。结果表明:由于典型地物端元提取中不可避免的误差及柑橘冠层覆盖度的差异,柑橘种植的准确识别与其柑橘端元丰度阈值存在对应关系。在经过反复试验的条件下,研究区柑橘端元丰度阈值设定在0.30~0.45范围之内,总精度达到90%以上,能够满足柑橘种植识别要求。     

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。     

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。     

高光谱遥感城市植被胁迫监测研究

开发有效的城市植被胁迫监测方法对于林业资源管理和营造良好的城市生态环境具有重要意义。采用EO-1卫星过境广州市东边建成区所采集的Hyperion高光谱影像,通过选取合适的植被指数进行分类,以及混合像元分解获得植被丰度这两种方法进行植被胁迫的识别,对比两者实验结果表明:在植被信息提取中植被丰度的方法要比指数法可靠且精度高;通过地面光谱测量,说明基于植被光谱理论的丰度分析能更好地表示植被胁迫的特征,为城市林业管理提供定性和定量的研究应用。     

一种无监督高光谱图像分类算法

为了实现对无任何先验知识的高光谱遥感数据的全自动分类,提出了一种关于高光谱图像的无监督分类算法。该算法将高光谱图像的凸面几何特征与光谱特征相结合,通过自动提取端元,并利用所提取的端元进行类别识别来实现高光谱图像的自动分类。此算法的特点是原理简单、易于实现、适应性广,而且不需要任何辅助支持和人工干预。实验结果表明,该算法能够获得较好的分类效果。     

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.     

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.     

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%).     

Utilization of Hyperion data over Dongargarh,India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.     

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.     

Vegetation classification method with biochemical composition estimated from remote sensing data

In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLC_A) and selected bands (MLC_S), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95.2%) was as good as MLC_S (95.2%) and better than MLC_A (91.1%), as was the kappa value (BCC 92.849%, MLC_S 92.845%, MLC_A 86.637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics.     

Discrimination of sedimentary lithologies using Hyperion and Landsat Thematic Mapper data: a case study at Melville Island,Canadian High Arctic

The use of remote-sensing techniques in the discrimination of rock and soil classes in northern regions can support a diverse range of activities, such as environmental characterization, mineral exploration and the study of Quaternary paleoenvironments. Although images with low spectral resolution can commonly be used in the mapping of classes possessing distinct spectral properties, hyperspectral images offer greater potential for discrimination of materials characterized by more subtle reflectance properties. In an effort to better constrain the utility of broadband and hyperspectral datasets in high-latitude research, this study investigated the effectiveness of Landsat Thematic Mapper (TM) and EO-1 Hyperion data for discrimination of lithological classes at eastern Melville Island, Nunavut, Canada. TM data were classified using a standard neural-network algorithm, and both TM and Hyperion data were linearly unmixed using ground-truth spectra. TM classification results successfully discriminate between classes over much of the study area, although with incomplete separation between clastic and carbonate materials. TM unmixing results are poor, with useful class separation restricted to vegetation and red-weathered sandstone classes. Hyperion results effectively depict the fractional cover of end members, although the abundance images of several classes contain background abundance values that overestimate surface exposure in some areas. For the study area and surface classes involved, noisy hyperspectral data were found to be of greater utility than higher-fidelity broadband multispectral data in the generation of fractional abundance images for an inclusive set of surface-cover classes.     

Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor

Mapping tools are needed to document the location and extent of Phragmites australis, a tall grass that invades coastal marshes throughout North America, displacing native plant species and degrading wetland habitat. Mapping Phragmites is particularly challenging in the freshwater Great Lakes coastal wetlands due to dynamic lake levels and vegetation diversity. We tested the applicability of Hyperion hyperspectral satellite imagery for mapping Phragmites in wetlands of the west coast of Green Bay in Wisconsin, U.S.A. A reference spectrum created using Hyperion data from several pure Phragmites stands within the image was used with a Spectral Correlation Mapper (SCM) algorithm to create a raster map with values ranging from 0 to 1, where 0 represented the greatest similarity between the reference spectrum and the image spectrum and 1 the least similarity. The final two-class thematic classification predicted monodominant Phragmites covering 3.4% of the study area. Most of this was concentrated in long linear features parallel to the Green Bay shoreline, particularly in areas that had been under water only six years earlier when lake levels were 66 cm higher. An error matrix using spring 2005 field validation points (n = 129) showed good overall accuracy—81.4%. The small size and linear arrangement of Phragmites stands was less than optimal relative to the sensor resolution, and Hyperion's 30 m resolution captured few if any pure pixels. Contemporary Phragmites maps prepared with Hyperion imagery would provide wetland managers with a tool that they currently lack, which could aid attempts to stem the spread of this invasive species.     

Significance of lineament patterns in rock unit classification and designation: a pilot study on the Gharib‐Dara area,northern Eastern Desert,Egypt

Satellite images and aerial photos are among continuous sources of data for mapping lineaments which frequently reflect surfaces of discontinuity in the rocks. The analysis of lineaments not only provides a method for detecting past tectonic trends but also helps in the exploration of minerals, oil and ground water and the seismic risk assessment for nuclear sites and repository studies as well.

In the present study, lineaments have been extracted from the digital satellite scene (Landsat 7, ETM⁺ data) for the Gebel Gharib‐Dara area using GeoAnalyst PCI EASI/PACE software. In a small test area, the lineaments that were digitally extracted for different settings of the GeoAnalyst parameters were compared with the visually interpreted lineaments for optimal settings of the parameters for lineament extraction.

The visual interpretation of the present false colour composite map (FCC) led to tentatively classify the rocks into 24 units to study the spatial distribution of the extracted lineaments. Lineament patterns in the form of lineament azimuth profiles (LAPs) are prepared and they are very characteristic for each unit. The NE lineament trend predominates over all the rock units while the NW trend characterizes those units located towards the Gulf of Suez. LAPs show that the younger granitic rock units of Gebel Gharib, Gebel Abu Khashaba and Gebel Dara (having relatively high radioactivity) are characterized by short amplitude of NW trends relative to the NE trends.

Correlating lineament density maps (LDM) with aeroradiometry total count contour maps and other ground radiometric maps show that rock units with high radioactivity are also characterized by high lineament density and lineament intersection density. Using the same reasoning, new possible uranium targets have been located.

The present study also shows that the younger granitoid is the only rock unit which has been classified into four sub‐lithologic units—G1, G2, G3 and G4—and they probably have a compositional variation that needs further field checking.