Monitoring active volcanism with the Autonomous Sciencecraft Experiment on EO-1

The ability to monitor and rapidly react to remote detection of volcanic activity has been greatly improved through use of the Autonomous Sciencecraft Experiment (ASE), an advanced software application installed on a spacecraft in Earth orbit. ASE is a NASA New Millennium Program experiment demonstrating science-driven autonomous command and control of a spacecraft. Flying on the Earth Observing-1 (EO-1) spacecraft, ASE successfully detected thermal emission from the Mt. Erebus lava lake on 7 May 2004, having analyzed a Hyperion hyperspectral data product on board the spacecraft. EO-1 was re-tasked by ASE to obtain a follow-up observation 7 h later and sent a notification of detection of volcanic activity to the ground. The entire process was carried out autonomously. Initial acquisition to receipt on the ground of the positive detection took less than 3 h, a process that without ASE would have taken weeks. The ASE Thermal Classifier has detected several styles of effusive volcanic activity: active lava lakes, pahoehoe flow fields, open channel flows and lava domes. ASE successfully demonstrated that science-driven spacecraft operation greatly enhances science return per returned byte through the identification of the most valuable data, allowing prioritization of downlink products and the discarding of null data sets. This technology has applications on missions elsewhere in the solar system. Modified thermal classifiers can be used for detecting and monitoring active volcanism on the jovian satellite Io, the neptunian moon Triton, and searching for active volcanism on Mars and icy satellites. The success of ASE is an incentive for future instrument and mission designers to consider on-board data-processing requirements (especially data storage capacity, number of processors and processor speed, and RAM) in order to take advantage of this flight-proven technology.     

Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1

On-board detection of cryospheric change in sea ice, lake ice, and snow cover is being conducted as part of the Autonomous Sciencecraft Experiment (ASE), using classifiers developed for the Hyperion hyper-spectral visible/infrared spectrometer on-board the Earth Observing-1 (EO-1) spacecraft. This classifier development was done with consideration for the novel limitations of on-board processing, data calibration, spacecraft targeting error and the spectral range of the instrument. During on-board tests, these algorithms were used to measure the extent of cloud, snow, and ice cover at a global suite of targets. Coupled with baseline imaging, uploaded thresholds were used to detect cryospheric changes such as the freeze and thaw of lake ice and the formation and break-up of sea ice. These thresholds were used to autonomously trigger follow-up observations, demonstrating the capability of the technique for future planetary missions where downlink is a constrained resource and there is high interest in data covering dynamic events, including cryospheric change. Before upload classifier performance was assessed with an overall accuracy of 83.3% as measured against manual labeling of 134 scenes. Performance was further assessed against field mapping conducted at Lake Mendota, Wisconsin as well as with labeling of scenes that were classified during on-board tests.     

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。     

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 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.     

地球观测1号高光谱与全色图像融合的最佳方法

受制于成像原理及制造技术等因素,航天高光谱遥感图像的空间分辨率相对较低,为此提出将高光谱图像与高空间分辨率图像进行融合处理,设计最佳的增强高光谱遥感图像空间分辨率的融合算法。针对地球观测1号(EO-1)Hyperion高光谱图像和高级陆地成像仪（ALI）全色波段图像的特点,从9种具体遥感图像融合算法中选用4种融合算法开展山区与城市的数据融合实验,即Gram-Schmidt光谱锐化融合法、平滑调节滤波(SFIM)变换融合法、加权平均法（WAM）融合法和小波变换（WT）融合法,并分别从定性、定量和分类精度三方面对这些方法的融合效果进行综合评价与对比分析,从而确定适合EO-1高光谱与全色图像融合的最佳方法。实验结果显示：从图像融合效果看,在所采用的4种融合方法中,Gram-Schmidt光谱锐化融合法的效果最好;从图像分类效果看,基于融合图像的分类效果要优于基于源图像的分类效果。理论分析与实验结果均表明：Gram-Schmidt光谱锐化融合法是一种较为理想的高光谱与高空间分辨率遥感图像的融合算法,为提高高光谱遥感图像的清晰度、可靠性及图像的地物识别和分类的准确性提供有力的支持。     

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。     

Detection of temporary flooded areas with potential floodwater mosquito production using imaging radar

Temporarily flooded areas can produce enormous numbers of floodwater mosquitoes, causing tremendous nuisance to people living in the vicinity. The aim of this study is to develop a remote-sensing method for detecting temporary flooded areas that can produce floodwater mosquitoes. For this objective, synthetic aperture radar (SAR) imagery from the European Remote Sensing satellite (ERS-2) and the Environmental Satellite (Envisat) are chosen. The images cover both flooded and dry periods around Lake Färnebofjärden, located in the lowlands of the River Dalälven, central Sweden, during the vegetation season between 2000 and 2006. Unsupervised classification and principal component analysis (PCA) are tested as methods for detecting floodwater mosquito production sites. In the unsupervised classification experiment, four types of images are tested. The classification of a synthetic colour image gives the best result with an overall accuracy of 85.7% and a kappa value of 0.7, as well as a 46% detection rate of field-mapped flooded areas. PCA is performed on a data set of 16 time series radar images. The resulting principal component (PC) bands provide information about flooding probability as well as vegetation structures. Regular flooding increases the probability for an area to provide breeding sites for floodwater mosquitoes. Thus, this approach will be very useful in estimating the risk of floodwater mosquito establishment.     

Hyperspectral spaceborne imaging of dust-laden flows: Anatomy of Saharan dust storm from the Bodélé Depression

We study hyperspectral images of the Bodélé Depression in Northern Chad, acquired by the Hyperion sensor onboard EO-1 spacecraft. Relative abundances of four major mineral components are obtained on a pixel-by-pixel basis and we report on the comparison of images of a dust storm with the same areas on a calm day. Minerals lifted and suspended particles downwind of a dust source are thus identified. Linear Spectral Unmixing (LSU) decomposition results for the calm condition match those of our field study. LSU calm vs. stormy comparison, based on absorbance features, highlight the spectral contrast. Despite low contrast above bright areas, morphological dissimilarity is evident via the wave and tongue-like features, aligned with the prevailing northeasterly winds. We analyze the longest part of shortwave infra-red (2080-2380 nm) wavelengths where the atmosphere is transparent, optical properties are stable, and absorption features of hydroxyl-bearing minerals, sulfates, and carbonates are pronounced. The results of our spectral analyses reveal that clay minerals may be used as tracers for atmospheric dust monitoring even above bright areas. Such clay minerals include kaolinite, illite-moscovite, and Fe-rich nontronite.     

Coupled correction and classification of hyperspectral imagery for mapping coral reefs of Agatti and Flat Islands,India

Among the various remote-sensing options available today to map ecomorphological classes of corals, hyperspectral remote sensing is one of the best options by virtue of its spectral capabilities, while high spatial resolution is a necessary condition to resolve finer morphological features spatially. Given high-spatial resolution data of equal to or better than 30 m, the discrimination capability of end-members of multi-/hyperspectral satellite data is dependent on the efficacy of the correction for atmospheric effects and the intervening water column. In this study, a coupled approach to account for oceanic and atmospheric radiative contributions, called the Coupled Ocean Atmosphere Radiative Transfer (COART), was applied to Earth Observing 1 (EO-1) mission Hyperion image data acquired over the coral reefs of Agatti Island in the Lakshadweep Islands, Arabian Sea and Flat Island in the Andaman Islands, Bay of Bengal, India. The paper presents an open-source approach to correct and perform unsupervised classification of Hyperion imagery using a custom-built software toolkit called HyperCorals. The study finds that Hyperion has sufficient capabilities for discrimination of a few ecomorphological classes and can be improved further by using coupled radiative transfer models. Correcting for the intervening water column helps in classifying submerged features. The k-means classification offers a simpler classification method to classify an image of a subset with 42 selected spectral channels of Hyperion in the visible and near infrared (VNIR) region than the traditional Iterative Self-Organizing Data Analysis Technique (ISODATA). The classification results using the cosine distance metric over 42 selected spectral channels of Hyperion in the VNIR region offer the potential to differentiate between various ecomorphological zones. The study also presents results from sensitivity analysis experiments and discusses the relative importance of three parameters: water column depth, bottom albedo, and chlorophyll concentration on the overall correction and classification of the imagery.     

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.     

Processing of Hyperion data set for detection of indicative minerals using a hybrid method in Dost-Bayli,Iran

In recent years hyperspectral remote sensing has played an important role in discovering of the earth surface and unmixing is an indispensable part of the hyperspectral data analysis. The most challenging stage in the spectral unmixing is determination of endmembers (EMs). Because of the absence of pure pixels, common methods based on pure pixel assumption do not yield accurate results. On the other hand, Hyperion hyperspectral data acquired by National Aeronautics and Space Administration (NASA)’s Earth Observing-1 (EO-1) system, is available widely but with a lower signal-to-noise ratio (SNR) in comparison to airborne spectrometers. Therefore, the methods with less sensitivity to noise amount will be more efficient in processing of the Hyperion data. Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF) algorithm is then an appropriate technique for EM detection in this case. It, however, has shortcomings in dealing with large data. To fix this problem the first module of Optical Real-time Adaptive Spectral Identification System (ORASIS) was applied for data reduction before running of the MVC-NMF algorithm. The modified technique was then investigated on a set of noisy synthetic data that the outcomes proved its functionality. The Hyperion image of Dost-Bayli area located in the Ardabil province in northwestern Iran, was then unmixed by the mentioned approach. To validate the accuracy of detected minerals, 20 surface samples were collected from the study area and analysed by X-ray diffraction (XRD) for detection of their mineralogical constituents and spectrometry to create a native spectral library. However, both native and United States Geological Survey (USGS) spectral libraries were applied in identification of estimated EMs. The signatures of the obtained EMs by hybrid method were appropriately similar to reference spectra. The mineral abundances maps were therefore generated by linear spectral unmixing (LSU), which have proper consistency with XRD results.     

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.     

谐波分析光谱角制图高光谱影像分类

目的 针对光谱角制图(SAM)分类算法对高光谱像元光谱曲线的局部特征和其辐射强度不敏感,而且易受噪声和维数灾难影响,致使分类效率低和精度较差等缺陷,将谐波分析(HA)技术引入到SAM高光谱影像分类中,提出一种基于谐波分析的光谱角制图(HA-SAM)高光谱影像分类算法.方法 利用HA技术将高光谱影像从光谱维变换到能量谱特征维空间,并提取低次谐波分量及特征系数(谐波余项、相位和振幅),用特征系数组成的向量代替光谱向量,对高光谱影像进行SAM分类.结果 将SAM和HA-SAM同时应用于EO-1卫星的Hyperion高光谱影像分类,通过对比和分析,验证了HA-SAM的优越性,再选择AVIRIS(airborne visible infrared imaging spectrometer)高光谱影像对HA-SAM进行验证,结果表明该算法具有较强的普适性.结论 HA-SAM提高了传统SAM高光谱影像分类的效率和精度,而且适用性较强具有良好的应用前景.     

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.     

基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度,针对传统特征选择方法信息损失大的缺陷,基于EO-1 Hyperion高光谱遥感影像,采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程,开展影像的地物分类实验研究,提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取,并以所提取的独立分量特征及其他地理辅助要素组成分类指标集;继而选择适当的指标组合和阈值设定判别规则,建立DTC模型进行影像的地物分类;最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看,前者可达89.34%,高出后者18.8%;从单一地物的分类精度看,前者仅水体的精度略低于后者,而其他11种地物的精度都高于后者。理论分析与实验结果均表明,ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。     

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.     

Ecological fingerprinting of ecosystem succession: Estimating secondary tropical dry forest structure and diversity using imaging spectroscopy

We evaluated the use of EO-1 Hyperion hyperspectral satellite imagery for mapping structure and floristic diversity in a Neotropical tropical dry forest as a way of assessing a region's ecological fingerprint. Analysis of satellite imagery provides a means to spatially appraise the dynamics of the structure and diversity of the forest. We derived optimal models for mapping canopy height, live aboveground biomass, Shannon diversity, basal area and the Holdridge Complexity Index from a dry season image. None of the evaluated models adequately estimated stem or species density. Due to the dynamic nature of the leaf phenology we found that for the application of remote sensing in Neotropical dry forests, the spectro-temporal domain (changes in the spectral signatures over time-season) must be taken into account when choosing imagery. The analyses and results presented here provide a means for rapid spatial assessment of structure and diversity characteristics from the microscale site level to an entire region.     

Characterization of a sulfur-rich Arctic spring site and field analog to Europa using hyperspectral data

A unique glacial spring system exists at Borup Fiord pass, in the Canadian High Arctic, emerging from a glacial surface and depositing elemental sulfur, gypsum and calcite across a portion of the glacier. The presence of sulfur springs associated with glacial ice is extremely rare in a terrestrial context, and the resulting deposits may provide a field analog to non-ice materials on the surface of Europa. Spectral characterization of the supraglacial deposits in the visible-near infrared (VIS-NIR) range, 0.4-2.5 µm, was carried out using reflectance spectra collected in situ using a field spectrometer during the 2006 field season. These spectra, while dominated by melting snow, ice, and sulfur, show that some absorption features of the sulfates are shifted in wavelength with respect to library spectra due to the effects of mixing or temperature. Absorption features of calcite are largely absent, potentially due to mineral partitioning effects within the deposits. Investigations into changes in mineralogy within the deposits over the course of the active season using data collected by the Hyperion hyperspectral visible/infrared spectrometer aboard the Earth Observing 1 spacecraft (EO-1) were limited by low signal-to-noise (SNR) ratios in the data, although they indicate that sulfur is remaining stable. This is confirmed by seasonal data on the extent of the deposits, obtained using a classification algorithm running onboard the satellite, which continued to detect the presence of sulfur until snow obscured the site. Ground truth for the observations is provided by mineralogical analyses obtained by X-ray diffraction (XRD) measurements and laboratory reflectance spectra from 0.2-25 µm obtained of samples returned from the site in 2006. We show that while sulfur, the main constituent of the deposits, is well represented in Hyperion data, minor constituents such as calcite and gypsum can be partially or entirely masked in the data. In spite of these effects, autonomous detection methods can be utilized to monitor the generation and extent of the deposits, whose spectral properties show similarities with those of Europa's non-ice materials.     

Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by ‘orange rust’ (Puccinia kuehnii) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660?nm) yielded increased separability of rust-affected areas. The newly formulated ‘Disease–Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.