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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery IV"

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Sample spectral correlation-based measures for subpixels and mixed pixels in real hyperspectral imagery

Sample spectral correlation-based measures for subpixels and...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Liu, Weimin Chang, Chein-, I Univ Maryland Baltimore Cty Remote Sensing Signal & Image Proc Lab Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)0819462896

A hyperspectral imaging sensor images a scene using hundreds of contiguous spectral channels to uncover many substances that cannot be resolved by multspectral sensors with tens of discrete spectral channels. Many spectral measures used for target discrimination and identification in hyperspectral imagery have been derived directly from multsispectral imagery rather than from a hyperspectral imagery viewpoint. This paper demonstrates that on many occasions such spectral measures are generally not effective when it is applied to real hyperspectral data for discrimination and identification due to the fact that they do not take into account the very high sample spectral correlation (SSC) provided by hyperspectral sensors. In order to address this issue, two approaches, referred to as a priori sample spectral correlation (PR-SSC) and a posteriori SSC (PSSSC) arc developed to account for spectral variability within real data to achieve better target discrimination and identification. While the former can be used to derive a family of a priori hyperspectral measures via orthogonal subspace projection (OSP) to eliminate interfering effects caused by undesired signatures, the latter results in a family of a posteriori hyperspectral measures that include sample covariance/correlation matrix as a posteriori information to increase ability in discrimination and identification. Interestingly, some well-known measures such as Euclidean distance (ED) and spectral angle mapper (SAM) can be shown to be special cases of the proposed PR-SSC and PS-SSC hyperspectral measures.

关键词： Euclidean distance (ED) orthogonal projection divergence (OPD) orthogonal subspace projection (OSP) pixel level-based spectral measures a posteriori sample spectral correlation (PS-SSC) a priori sample spectral correlation (PR-SSC) sample spectral correlation (SSC) spectral angle mapper (SAM) spectral information divergence (SID)

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A classification approach and comparison to other object identification algorithms for hyperspectral imagery

A classification approach and comparison to other object ide...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Mayer, R. Antoniades, J. Baumback, M. Chester, D. Edwards, J. Goldstein, A. Haas, D. Henderson, S. BAE Syst ATI Washington DC 20037 USA

ISBN: (纸本)0819462896

This study adapts a variety of techniques derived from multi-spectral image classification to find objects amid cluttered backgrounds in hyperspectral imagery. This study quantitatively compares the algorithms against a standard object search, the matched filter (MF) and recently developed object detector, Adaptive Cosine Estimator (ACE). These object searches require calculating the Mahalanobis distance between the average object spectral signature and the test pixel spectrum and needs the computation of a covariance matrix. The covariance matrix is generated using the entire image (Whitened Euclidean Distance, WED) or using pixels associated with the object (Maximum Likelihood Classifier, MLC). The latter computation requires a relatively large number of pixels to generate a non-singular, accurate covariance matrix. Regularizing object pixels via optimally mixing (likelihood maximization) diagonal, object, and entire image covariance matrices to generate the object covariance matrix estimate. This approximation is called the Regularized Maximum Likelihood Classifier (RMLC). The object searches MF, ACE, WED, MLC, and RMLC were applied to visible/near IR data collected from forest and desert environments. This study searched for objects using object signatures and covariance matrices taken directly from the scene and from statistically transformed object signatures and covariance matrices from another time. This study found a substantial reduction in the number of false alarms (factor of 10 to 1000) using WED, ACE, RMLC relative to MF searches for the two independent data collects. The regularization of in-scene and transformed covariance matrices substantially reduced false alarms relative to using unprocessed covariance matrices. This study adds simple, high performing algorithms to the object search arsenal.

关键词： target detection object identification hyperspectral imagery matched filters

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Design and modelling of spectral-thermal unmixing targets for airborne hyperspectral imagery

Design and modelling of spectral-thermal unmixing targets fo...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Clare, Phil Def Sci & Technol Lab Farnborough GU14 0LX Hants England

ISBN: (纸本)0819462896

Techniques to determine the proportions of constituent materials within a single pixel spectrum are well documented in the reflective (0.4-2.5 mu m) domain. The same capability is also desirable for the thermal (7-14 mu m) domain, but is complicated by the thermal contributions to the measured spectral radiance. Atmospheric compensation schemes for the thermal domain have been described along with methods for estimating the spectral emissivity from a spectral radiance measurement and hence the next stage to be tackled is the unmixing of thermal spectral signatures. In order to pursue this goal it is necessary to collect data of well-calibrated targets which will expose the limits of the available techniques and enable more robust methods to be designed. This paper describes the design of a set of ground targets for an airborne hyperspectral imager, which will test the effectiveness of available methods. The set of targets include panels to explore a number of difficult scenarios such as isothermal (different materials at identical temperature), isochromal (identical materials, but at differing temperatures), thermal adjacency and thermal point sources. Practical fabrication issues for heated targets and selection of appropriate materials are described. Mathematical modelling of the experiments has enabled prediction of at-sensor measured radiances which are used to assess the design parameters. Finally, a number of useful lessons learned during the fielding of these actual targets are presented to assist those planning future trials of thermal hyperspectral sensors.

关键词： thermal hyperspectral targets unmixing emissivity

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Vehicle tracking with multi-temporal hyperspectral imagery

Vehicle tracking with multi-temporal hyperspectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Kerekes, John Muldowney, Michael Strackerjan, Kristin Smith, Lon Leahy, Brian Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Digital Imaging & Remote Sensing Lab 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)0819462896

hyperspectral imagery has the capability of capturing spectral features of interest that can be used to differentiate among similar materials. While hyperspectral imaging has been demonstrated to provide data that enable classification of relatively broad categories, there remain open questions as to how fine of discrimination is possible. An application of this fine discrimination question is the potential that spectral features exist in the surface reflectance of ordinary civilian vehicles that would enable tracking of a particular vehicle across repeated hyperspectral images in a cluttered urban area. To begin to explore this question a vehicle tracking experiment was conducted in the summer of 2005 on the Rochester Institute of Technology (RIT) campus in Rochester, New York. Several volunteer vehicles were moved around campus at specific times coordinated with over flights of RIT's airborne Modular Imaging Spectrometer Instrument (MISI). MISI collected sequential images of the campus in 70 spectral channels from 0.4 to 1.0 microns with a ground resolution of approximately 2.5 meters. Ground truth spectra and photographs were collected for the vehicles. These data are being analyzed to determine the ability to uniquely associate a vehicle in one image with its location in a subsequent image. Initial results have demonstrated that the spectral measurement of a specific vehicle can be used to find the same vehicle in a subsequent image, although this is not always possible and is very dependent upon the specifics of the situation. Additionally, efforts are presented that explore predicted performance for variations in scene and sensor parameters through an analytical performance prediction model.

关键词： hyperspectral vehicle detection target tracking

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Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery

Land surface temperature and emissivity retrieval from therm...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Boonmee, Marvin Schott, John R. Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Digital Imaging & Remote Sensing Lab 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)0819462896

A new algorithm, optimized land surface temperature and emissivity retrieval (OLSTER), was developed to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve both natural and man-made materials. Multi-directional or multi-temporal observations are not required, and the scenes do not have to be dominated by blackbody features. The OLSTER algorithm consists of a preprocessing step, an iterative near-blackbody pixels search, and an iterative constrained optimization loop. The preprocessing step provides initial estimates of LST per pixel and the atmospheric parameters of transmittance and upwelling radiance for the entire image. Pixels that are under- or over-compensated for the atmospheric parameters are classified as near-blackbody and lower emissivity pixels, respectively. A constrained optimization of the atmospheric parameters using generalized reduced gradients on the near-blackbody pixels ensures physical results. The downwelling radiance is estimated from the upwelling radiance by applying a look-up table of coefficients based on a polynomial regression of radiative transfer model runs for the same sensor altitude. The LST and emissivity per pixel are retrieved simultaneously using the well established ISSTES algorithm. The OLSTER algorithm can retrieve LST within about +/- 2.0 K, and emissivities within about +/- 0.01 based on numerical simulation.

关键词： atmospheric compensation hyperspectral land surface temperature temperature and emissivity separation thermal infrared

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Analysis of LIDAR and spectral imagery for scene classification in forested areas

Analysis of LIDAR and spectral imagery for scene classificat...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Helt, Michael Olsen, R. C. Puetz, Angela M. US Naval Postgrad Sch 833 Dyer Rd Monterey CA 93943 USA

ISBN: (纸本)0819462896

LIDAR data taken over the Elkhorn Slough in Central California were analyzed for terrain classification. The specific terrain element of interest is vegetation, and in particular, tree type. Data taken on April 12th, 2005, were taken over a 10 km x 20 km region which is mixed use agriculture and wetlands. Time return and intensity were obtained at similar to 2.5 in postings. Multi-spectral imagery from QuickBird was used from a 2002 imaging pass to guide analysis. Ground truth was combined with the orthorectified satellite imagery to determine regions of interest for areas with Eucalyptus, Scrub Oak, Live Oak, and Monterey Cyprus trees. LIDAR temporal returns could be used to distinguish regions with trees from cultivated and bare soil areas. Some tree types could be distinguished on the basis of the relationship between first/last extracted feature returns. The otherwise similar Eucalyptus and Monterey Cyprus could be distinguished by means of the intensity information from the imaging LIDAR. The combined intensity and temporal data allowed accurate distinction between the tree types, a task not otherwise practical with the satellite spectral imagery.

关键词： LIDAR terrain classification tree type identification

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Applications of independent component analysis (ICA) in endmember extraction and abundance quantification for hyperspectral imagery

Applications of independent component analysis (ICA) in endm...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Wang, Jing Chang, Chein-, I Univ Maryland Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819462896

independent component analysis (ICA) has shown success in many applications. This paper investigates a new application of the ICA in endmember extraction and abundance quantification for hyperspectral imagery. An endmember is generally referred to as an idealized pure signature for a class whose presence is considered to be rare. When it occurs, it may not appear in large population. In this case, the commonly used principal components analysis (PCA) may not be effective since endmembers usually contribute very little in statistics to data variance. In order to substantiate our findings, an ICA-based approach, called ICA-based abundance quantification algorithm (ICA-AQA) is developed. Three novelties result from our proposed ICA-AQA. First, unlike the commonly used least squares abundance-constrained linear spectral mixture analysis (ACLSMA) which is a 2(nd) order statistics-based method, the ICA-AQA is a high order statistics-based technique. Second, due to the use of statistical independence it is generally thought that the ICA cannot be implemented as a constrained method. The ICA-AQA shows otherwise. Third, in order for the ACLSMA to perform abundance quantification, it requires an algorithm to find image endmembers first then followed by an abundance-constrained algorithm for quantification. As opposed to such a two-stage process, the ICA-AQA can accomplish endmember extraction and abundance quantification simultaneously in one-shot operation. Experimental results demonstrate that the ICA-AQA performs at least comparably to abundance-constrained methods.

关键词： Independent component analysis hyperspectral imaging Algorithm development Minerals Digital imaging Image processing Neodymium Principal component analysis Spatial resolution Pixel resolution

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A new approach to infer surface emissivity parameters from longwave infrared hyperspectral measurements

A new approach to infer surface emissivity parameters from l...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Lausten, Kevin M. Resmini, Ronald G. Natl Geospatial Intelligence Agcy Washington DC 22150 USA

ISBN: (纸本)0819462896

A new approach to perform temperature/emissivity separation (TES) has been developed and is described in this paper. The Planck-Modeled Temperature Emissivity Separation (PM-TES) technique provides a unique approach to the derivation of surface emissivity parameters from longwave infrared (LWIR) hyperspectral imagery (HSI) without incorporating the direct assumptions required in most current TES techniques. Accurate calculation of emissivity from at-sensor radiance values is complicated by the impact of temperature on the detected signal. Known are the ground leaving radiance (GLR) values observed by the sensor (after atmospheric compensation), while unknown are the emissivity values and the temperature of the surface material. Emissivity is difficult to determine at this juncture because there are N+1 unknown terms (N being the number of spectral bands contained in the HSI dataset) and N known terms, resulting in an indeterminate problem. Prior methods have incorporated assumptions associated with one of the unknown terms, which allows for the calculation of all additional unknown terms (e.g., Kahle and Alley, 1992)(1). While these techniques have shown success in a variety of circumstances, the accuracy of their results is most often dependant upon a singular assumption. Minor miscalculations in these assumptions are thus propagated through the resultant HSI dataset for the extent of spectral processing. The PM-TES technique proposed in this paper does not make specific assumptions about either the temperature or emissivity characteristics of the in-scene materials to calculate emissivity parameters which are comparable in accuracy to current standard industry techniques. The proposed technique requires the input of temperature and emissivity ranges over which the Planck function is modeled to solve for the relationship between GLR and emissivity terms.

关键词： PM-TES TES LWIR SEBASS temperature-emissivity separation HSI hyperspectral imagery

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Signature evolution with covariance equalization in oblique hyperspectral imagery

Signature evolution with covariance equalization in oblique ...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XII

作者： Leathers, Robert A. Schaum, Alan P. Downes, Trijntje Valerie US Navy Res Lab Opt Sci Div Code 56004555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)0819462896

Covariance equalization (CE) is a method by which one can predict the change in an objects's hyperspectral signature due to changes in sun position, atmospheric conditions, and viewing angle and range. Specifically, CE produces a linear transformation that relates the object's signature as measured at the sensor at a particular time to that measured at another time and under different conditions. The transformation is based on the background statistics of a scene imaged at the two times. Although CE was derived under the assumption that the two images cover mostly the same geographic area, it also has been found to work well for objects that have moved from one location to another. The CE technique has been previously verified with data from a nadir-viewing visible hyperspectral camera. In this paper, however, we show results from the application of CE to highly oblique hyperspectral SWIR data. We evaluate the utility of CE primaily through its effectiveness in transforming signatures acquired under one set of conditions for application to matched-filter object detection under a second set of conditions (e.g., view angle, slant range, altitude, atmospheric conditions, and time of day). Object detection with highly oblique sensors (75 deg. to 80 deg. off-nadir) is far more difficult than with nadir-viewing sensors for several reasons: increased atmospheric optical thickness, which results in lower signal-to-noise and higher adjacency effects;fewer pixels on object;the effects of the nonuniformity of the bidirection reflectance function of most man-made objects;and the change in pixel size when measurements are taken at different slant ranges.

关键词： hyperspectral oblique object detection matched filter covariance equalization atmospheric compensation

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Matched filters for multispectral point target detection

Matched filters for multispectral point target detection

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conference on Imaging Spectrometry XI

作者： Buganim, S. Rotman, S. R. Ben Gurion Univ Negev Dept Electroopt Engn Beer Sheva Israel

ISBN: (纸本)0819463817

Spectral signatures derived from a multispectral or hyperspectral imager can be used in matched filter algorithms to help distinguish targets from background. In this paper we demonstrate the use of these matched filters for different target implantation models. We show that even though a specific matched filter is designated for a particular implantation model, we can use other matched filters and obtain higher detection values for low false alarm rates. We evaluate the efficiency of the algorithms by systematically implanting the target's signature into every pixel in the image and obtaining its score;the lowest scores are those pixels in which the target may be missed. For every algorithm, we generate histograms for the no-target and target cases and then analyze using the classical ROC curve.

关键词： point target detection multispectral imagery

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