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

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Proceedings of SPIE: algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery ViiI

Proceedings of SPIE: Algorithms and Technologies for Multisp...

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algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery ViiI

This Volume 4725 of the conference proceedings contains 59 papers. Topics discussed include band selection, endmember selection and feature extraction, emissive remote sensing applications, atmospheric characterization and correction, spectral applications and methodology, detection and identification, clustering and classification, multispectral thermal imager, atmospheric characterization and correction, imaging spectrometry projects and sharpening and compression.

关键词： Spectrum analysis

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Empirical radiometric correction of optical remote sensing imagery

Empirical radiometric correction of optical remote sensing i...

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

作者： Palubinskas, G Müller, R Reinartz, P DLR German Aerosp Ctr D-82234 Wessling Germany

ISBN: (纸本)0819444758

We propose an empirical radiometric correction method for the effects, such as atmospheric effects and anisotropic reflection of the surface, in optical remote sensing data. These distortions are sensor viewing (scanning) angle dependent, thus they can be significant for data received from airborne sensors due to their wide field of view. The procedure is based solely on the digital image data and consists of several steps. First, the initial image region near nadir (minimal distortions) is clustered by an extended k-means algorithm, which automatically detects the clusters (surface types) in an image. Then, for each cluster an average line profile is calculated. These profiles (initially defined in a middle part of an image fine) are extrapolated to the whole line of an image by a polynomial approximation. Finally, from these polynomial functions the linear regression over all clusters is build using the radiative transfer equation, which allows the radiometric correction for each viewing angle in an image relative to the reference angle, usually nadir. The procedure is iterative, that is the correction is first performed for a narrow part around the initial region. Then the procedure is initialized with this newly corrected image region and repeated until the whole image is corrected. The experiments for data acquired by airborne multispectral scanner DAEDALUS AADS 1268 ATM show the effectiveness of the proposed method especially for the mosaicking and classification applications.

关键词： image-based atmospheric correction sensor viewing angle effects BRDF effects airborne scanner data

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Evolving feature extraction algorithms for hyperspectral and fused imagery

Evolving feature extraction algorithms for hyperspectral and...

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5th International conference on Information Fusion, FUSION 2002

作者： Brumby, Steven P. Pope, Paul A. Galbraith, Amy E. Szymanski, John J. Space and Remote Sensing Sciences Los Alamos National Laboratoty Mail Stop D436 Los Alamos NM 87545 United States

ISBN: (纸本)0972184414

hyperspectral imagery with moderate spatial resolution (/spl sim/30 m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatio-spectral feature extraction pipelines from training data provided via an intuitive, point-and-click interface. We describe recent work exploring geospatial feature extraction from hyperspectral imagery, and from a multi-instrument fused dataset. For hyperspectral imagery, we demonstrate our system on NASA Earth Observer 1 (EO-1) Hyperion imagery, applied to agricultural crop detection. We present an evolved pipeline, and discuss its operation. We also discuss work with multi-spectral imagery (DOE/NNSA multispectral Thermal Imager) fused with USGS digital elevation model (DEM) data, with the application of detecting mixed conifer forest. © 2002 Int. Soc. of Information Fusion.

关键词： Genetic programming

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Exploiting MODTRAN radiation transport for atmospheric correction: The FLAASH algorithm

Exploiting MODTRAN radiation transport for atmospheric corre...

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5th International conference on Information Fusion (FUSION 2002)

作者： Berk, A Adler-Golden, SM Ratkowski, AJ Felde, GW Anderson, GP Hoke, ML Cooley, T Chetwynd, JH Gardner, JA Matthew, MW Bernstein, LS Acharya, PK Miller, D Lewis, P Spectral Sci Inc Burlington MA 01803 USA

ISBN: (纸本)0972184422

Terrain categorization and target detection algorithms applied to hyperspectral imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affected by variations in lighting conditions. Atmospheric Correction referred to as Atmospheric 'Compensation', 'Characterization', etc) algorithms (ACAs) are used in applications of remotely sensed HSI data to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is an ACA createdfor HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its 'physics-based' mathematics from MODTRAN4.

关键词： hyperspectral imagery radiative transfer remote sensing MODTRAN FLAASH Atmospheric Correction

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Evolving feature extraction algorithms for hyperspectral and fused imagery

Evolving feature extraction algorithms for hyperspectral and...

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International conference on Information Fusion

作者： S.P. Brumby P.A. Pope A.E. Galbraith J.J. Szyinanski Space and Remote Sensing Sciences Los Alamos National Laboratory Los Alamos New Mexico USA Los Alamos National Laboratory Los Alamos NM US Space & Remote Sensing Sci. Los Alamos Nat. Lab. NM USA

关键词： Feature extraction hyperspectral imaging Pipelines Spatial resolution Genetic programming Software systems Humans Training data NASA Earth

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An automatic target recognition system for hyperspectral imagery using ORASIS

An automatic target recognition system for hyperspectral ima...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Gillis, D Palmadesso, P Bowles, J USN Res Lab Remote Sensing Div Washington DC 20375 USA

ISBN: (纸本)0819440760

We present an automatic target recognition system (ATR) for hyperspectral imagery. The system has been designed to use the output from ORASIS (the Optical Real-time Adaptive Spectral Identification System), a hyperspectral analysis package designed at the Naval Research Laboratory. The ATR system is capable of performing both target recognition (including subpixel identification) and anomaly detection, in near real-time and with no a priori scene knowledge. In this paper we discuss the algorithms we use in the ATR and include experimental results using the HYDICE Forest Radiance data set.

关键词： hyperspectral analysis ATR ORASIS

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Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery

Shadow-insensitive material detection/classification with at...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Adler-Golden, SM Levine, RY Matthew, MW Richtsmeier, SC Bernstein, LS Gruninger, J Felde, G Hoke, M Anderson, GP Ratkowski, A Spectral Sciences Inc. (United States) Air Force Research Lab. (United States)

ISBN: (纸本)0819440760

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sun and by the sky. Some specific algorithm and applications are illustrated using hyperspectral Digital imagery Collection Experiment (HYDICE) data.

关键词： detection classification hyperspectral image shadow

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Evolving forest fire burn severity classification algorithms for multi-spectral imagery

Evolving forest fire burn severity classification algorithms...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Brumby, SP Harvey, NR Bloch, JJ Theiler, J Perkins, S Young, AC Szymanski, JJ Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819440760

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

关键词： multispectral imagery genetic programming supervised classification forest fire wildfire

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algorithms for multispectral, hyperspectral, and ultraspectral imagery Vii

Algorithms for multispectral, hyperspectral, and ultraspectr...

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algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

The proceedings contains 59 papers from the conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii. Topics discussed include: spectral subspace matched filtering;automated optimal channel selection for spectral imaging sensors;band selection for lossless image compression;automated hyper/multispectral image analysis tools;and efficient materials mapping for hyperspectral data.

关键词： Image analysis

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Band selection for lossless image compression

Band selection for lossless image compression

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Hunt, SD Vélez-Reyes, M Univ Puerto Rico Mayaguez Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819440760

Lossless compression algorithms typically do not use spectral prediction, and typical algorithms that do, use only one adjacent band. Using one adjacent band has the disadvantage that if the last band compressed is needed, all previous bands must be decompressed. One way to avoid this is to use a few selected bands to predict the others. Exhaustive searches for band selection have a combinatorial problem, and are therefore not possible except in the simplest cases. To counter this, the use of a fast approximate method for band selection is proposed. The bands selected by this algorithm are a reasonable approximation to the principal components. Results are presented for exhaustive studies using entropy measures, sum of squared errors, and compared to the fast algorithm for simple cases. Also, it was found that using six bands selected by the fast algorithm produces comparable performance to one adjacent band.

关键词： image compression hyperspectral imagery band selection principal component analysis

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