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

共 408 条记录，以下是91-100 订阅

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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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A Comparison of Real and Simulated Airborne Multisensor imagery

A Comparison of Real and Simulated Airborne Multisensor Imag...

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20th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Bloechl, Kevin De Angelis, Chris Gartley, Michael Kerekes, John Nance, C. Eric Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Digital Imaging & Remote Sensing Lab 54 Lomb Mem Dr Rochester NY 14623 USA Raytheon Intelligence Informat & Serv Garland TX USA

ISBN: (纸本)9781628410259

This paper presents a methodology and results for the comparison of simulated imagery to real imagery acquired with multiple sensors hosted on an airborne platform. The dataset includes aerial multi- and hyperspectral imagery with spatial resolutions of one meter or less. The multispectral imagery includes data from an airborne sensor with three-band visible color and calibrated radiance imagery in the long-, mid-, and short-wave infrared. The airborne hyperspectral imagery includes 360 bands of calibrated radiance and reflectance data spanning 400 to 2450 nm in wavelength. Collected in September 2012, the imagery is of a park in Avon, NY, and includes a dirt track and areas of grass, gravel, forest, and agricultural fields. A number of artificial targets were deployed in the scene prior to collection for purposes of target detection, subpixel detection, spectral unmixing, and 3D object recognition. A synthetic reconstruction of the collection site was created in DIRSIG, an image generation and modeling tool developed by the Rochester Institute of Technology, based on ground-measured reflectance data, ground photography, and previous airborne imagery. Simulated airborne images were generated using the scene model, time of observation, estimates of the atmospheric conditions, and approximations of the sensor characteristics. The paper provides a comparison between the empirical and simulated images, including a comparison of achieved performance for classification, detection and unmixing applications. It was found that several differences exist due to the way the image is generated, including finite sampling and incomplete knowledge of the scene, atmospheric conditions and sensor characteristics. The lessons learned from this effort can be used in constructing future simulated scenes and further comparisons between real and simulated imagery.

关键词： Scene modeling hyperspectral multispectral target detection unmixing classification sensor performance

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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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Distributed source separation algorithms for hyperspectral image processing

Distributed source separation algorithms for hyperspectral i...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Robila, S Montclair State Univ Dept Comp Sci Montclair NJ 07043 USA

ISBN: (纸本)081945348X

This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multi spectral/hyperspectral imagery, the separated components can be associated with features present in the image, the source separation algorithm projecting them in different image bands. ICA based methods have been employed for target detection and classification of hyperspectral images. However, these methods involve an iterative optimization process. When applied to hyperspectral data, this iteration results in significant execution times. The time efficiency of the method is improved by running it on a distributed environment while preserving the accuracy of the results. The design of the distributed algorithm as well as issues related to the distributed modeling of the hyperspectral data were taken in consideration and presented. The effectiveness of the proposed algorithm has been tested by comparison to the sequential source separation algorithm using data from AVIRIS and HYDICE. Preliminary results indicate that, while the accuracy of the results is preserved, the new algorithm provides a considerable speed-up in processing.

关键词： hyperspectral imagery source separation Independent Component Analysis distributed processing

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Dimensionality reduction for spatial-spectral target detection on hyperspectral imagery 24

Dimensionality reduction for spatial-spectral target detecti...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Kaufman, Jason R. Meola, Joseph Univ Dayton Res Inst 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab 2211 Avion Circle Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

hyperspectral images often contain hundreds of spectral bands. Man-made anti natural materials usually exhibit variability their reflective and emissive across these bands, which is exploitable via target detection algorithms. The high dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.

关键词： hyperspectral imagery dimensionality reduction spatial-spectral feature extraction spatial-spectral target detection spectral target detection

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Regularization techniques and parameter estimation for object detection in hyperspectral data

Regularization techniques and parameter estimation for objec...

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

作者： Ramírez-Vélez, MD Jiménez-Rodríguez, LO Lab Appl Remote Sensing & Image Proc Mayaguez PR USA

ISBN: (纸本)0819449539

The main challenge for the retrieval of information using hyperspectral. sensors is that due to the high dimensionality provided by them there is not comparably enough, a priori data to produce well-estimated parameters to solve our detection problem. This lack of enough a priori information for an estimation yields to a rank-deficient problem. As a consequence, this leads to an increment in false alarms and increase in the probability of missing throughout the classification process. An approach based on a regularization technique applied to the data collected from the hyperspectral sensor is used to simultaneously minimize the probabilities of false alarms and missing. This procedure is implemented using algorithms that apply regularization techniques by biasing the covariance matrix, which enable the simultaneous reduction of the probability of false alarm and the decrease of the probability of missing;thus, enhancing the Maximum Likelihood parameter estimation.

关键词： remote sensing pattern recognition hyperspectral imagery maximum likelihood estimation parameter estimation covariance estimation regularization

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Restoration, target recognition, and countermeasure removal using the Computed Tomographic Imaging Spectrometer (CTIS)

Restoration, target recognition, and countermeasure removal ...

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

作者： Schau, HC Soulon, RE Hall, GL Garrood, DJ Raytheon Electronic Systems (United States)

Computed Tomographic Imaging Spectrometers are described and shown to be capable of providing real-time flash multispectral imagery. Restoration equations and techniques for optimizing performance are described. Exper... 详细信息

ISBN: (纸本)0819444758

关键词： imaging spectrometer multispectral imagery diffractive optical elements

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Sparse linear filters for detection and classification in hyperspectral imagery

Sparse linear filters for detection and classification in hy...

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

作者： Theiler, James Glocer, Karen Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM USA

ISBN: (纸本)0819462896

We investigate the use of convex optimization to identify sparse linear filters in hyperspectral. imagery. A linear filter is sparse if a large fraction of its coefficients are zero. A sparse linear filter can be advantageous because it only needs to access a subset of the available spectral channels, and it can be applied to high-dimensional data more cheaply than a standard linear detector. Finding good sparse filters is nontrivial because there is a combinatorially large number of discrete possibilities from which to choose the optimal subset of nonzero coefficients. But, by converting the optimality criterion into a convex loss function, and by employing an L1 penalty, one can obtain sparse solutions that are globally optimal. We investigate the performance of these sparse filters as a function of their sparsity, and compare the convex optimization approach with more traditional alternatives for feature selection. The methodology is applied both to the adaptive matched filter for weak signal detection, and to the Fisher linear discriminant for terrain categorization.

关键词： matched filter hyperspectral imagery adaptive signal detection

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hyperspectral chemical plume quantification via background radiance estimation

Hyperspectral chemical plume quantification via background r...

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

作者： Niu, Sidi Golowich, Steven E. Ingle, Vinay K. Manolakis, Dimitris G. Northeastern Univ 360 Huntington Ave Boston MA 02115 USA MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)9780819495341

Existing chemical plume quantification algorithms assume that the off-plume radiance of a pixel containing the plume signal is unobservable. When the problem is limited to a single gas, the off-plume radiance may be estimated from the bands in which the gas absorption is nearly zero. It is then possible to compute the difference between the on- and off-plume radiances and solve for the plume strength from Beer's Law. The major advantage of this proposed method is that the gas strength can be resolved from the radiance difference so that the estimation error remains small for thick plumes.

关键词： hyperspectral imagery narrow-band gas background estimation chemical gaseous agent quantification

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Target detection enhancement using temporal signature propagation

Target detection enhancement using temporal signature propag...

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

作者： Mayer, R Schaum, A SFA Inc Largo MD 20774 USA

ISBN: (纸本)0819436755

Changes in atmospheric conditions and sensor response for successive imaging sessions have limited the use of fixed target hyperspectral libraries to help discriminate targets from cluttered backgrounds. The hyperspectral target signature instability and unpredictability have resulted in a dependence on anomaly detection algorithms in real time surveillance applications. However, the performance of these techniques fails to meet system requirements for many military applications. Our study examines temporal variations in the long-wave infrared spectra of man-made targets and natural backgrounds obtained with the SEBASS (8-12 mu m) imager as part of the Dark HORSE 2 exercise during the HYDRA data collection in November, 1998. We examine the signature information propagated over various time intervals. In addition, the hyperspectral target signatures, taken over various time intervals, are transformed using image-based techniques and compared to those converted with methods based on atmospheric modeling. We show that detection performance can be dramatically improved by exploiting signature information propagated over various time intervals using image-based methods. Implications of this study for target detection enhancement using hyperspectral data derived from multiple flight missions and/or mathematical transformations of hyperspectral target signatures will be discussed.

关键词： automatic target detection algorithms for hyperspectral imagery remote sensing

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