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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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Generation of remotely sensed reference data using low altitude, high spatial resolution hyperspectral imagery 22

Generation of remotely sensed reference data using low altit...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Williams, McKay D. van Aardt, Jan Kerekes, John P. Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510600812

Exploitation of imaging spectroscopy (hyperspectral) data using classification and spectral unmixing algorithms is a major research area in remote sensing, with reference data required to assess algorithm performance. However, we are limited by our inability to generate rapid, accurate, and consistent reference data, thus making quantitative algorithm analysis difficult. As a result, many investigators present either limited quantitative results, use synthetic imagery, or provide qualitative results using real imagery. Existing reference data typically classify large swaths of imagery pixel-by-pixel, per cover type. While this type of mapping provides a first order understanding of scene composition, it is not detailed enough to include complexities such as mixed pixels, intra-endmember variability, and scene anomalies. The creation of more detailed ground reference data based on field work, on the other hand, is complicated by the spatial scale of common hyperspectral data sets. This research presents a solution to this challenge via classification of low altitude, high spatial resolution (1m GSD) National Ecological Observatory Network (NEON) hyperspectral imagery, on a pixel-by-pixel basis, to produce sub-pixel reference data for high altitude, lower spatial resolution (15m GSD) AVIRIS imagery. This classification is performed using traditional classification techniques, augmented by (0.3m GSD) NEON RGB data. This paper provides a methodology for generating large scale, sub-pixel reference data for AVIRIS imagery using NEON imagery. It also addresses challenges related to the fusion of multiple remote sensing modalities (e.g., different sensors, sensor look angles, spatial registration, varying scene illumination, etc.). A new algorithm for spatial registration of hyperspectral imagery with disparate resolutions is presented. Several versions of reference data results are compared to each other and to direct spectral unmixing of AVIRIS data. Initial results are p

关键词： Reference Data Ground Truth Imaging Spectroscopy Ehyperspectral imagery Spectral Unmixing Image Classification NEON AVIRIS

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A Parametric Study of Unsupervised Anomaly Detection Performance in Maritime imagery Using Manifold Learning Techniques 22

A Parametric Study of Unsupervised Anomaly Detection Perform...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Olson, C. C. Doster, T. Naval Res Lab Opt Sci Div 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781510600812

We investigate the parameters that govern an unsupervised anomaly detection framework that uses nonlinear techniques to learn a better model of the non-anomalous data. A manifold or kernel-based model is learned from a small, uniformly sampled subset in order to reduce computational burden and under the assumption that anomalous data will have little effect on the learned model because their rarity reduces the likelihood of their inclusion in the subset. The remaining data are then projected into the learned space and their projection errors used as detection statistics. Here, kernel principal component analysis is considered for learning the background model. We consider spectral data from an 8-band multispectral sensor as well as panchromatic infrared images treated by building a data set composed of overlapping image patches. We consider detection performance as a function of patch neighborhood size as well as embedding parameters such as kernel bandwidth and dimension. ROC curves are generated over a range of parameters and compared to RX performance.

关键词： hyperspectral Imaging Nonlinear Dimensionality Reduction Manifold Learning Anomaly Detection Unsupervised Learning Target Detection

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A spectral climatology for atmospheric compensation of hyperspectral imagery 22

A spectral climatology for atmospheric compensation of hyper...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Powell, John H. Resmini, Ronald G. George Mason Univ Coll Sci 4400 Univ Dr Fairfax VA 22030 USA

ISBN: (纸本)9781510600812

Most Earth observation hyperspectral imagery (HSI) detection and identification algorithms depend critically upon a robust atmospheric compensation capability to correct for the effects of the atmosphere on the radiance signal. Atmospheric compensation methods typically perform optimally when ancillary ground truth data are available, e.g., high fidelity in situ radiometric observations or atmospheric profile measurements. When ground truth is incomplete or not available, additional assumptions must be made to perform the compensation. Meteorological climatologies are available to provide climatological norms for input into the radiative transfer models;however no such climatologies exist for empirical methods. The success of atmospheric compensation methods such as the empirical line method suggests that remotely sensed HSI scenes contain comprehensive sets of atmospheric state information within the spectral data itself. It is argued that large collections of empirically-derived atmospheric coefficients collected over a range of climatic and atmospheric conditions comprise a resource that can be applied to prospective atmospheric compensation problems. A previous study introduced a new climatological approach to atmospheric compensation in which empirically derived spectral information, rather than sensible atmospheric state variables, is the fundamental datum. The current work expands the approach across an experimental archive of 127 airborne HSI datasets spanning nine physical sites to represent varying climatological conditions. The representative atmospheric compensation coefficients are assembled in a scientific database of spectral observations and modeled data. Improvements to the modeling methods used to standardize the coefficients across varying collection and illumination geometries and the resulting comparisons of adjusted coefficients are presented. The climatological database is analyzed to show that common spectral similarity metrics can be used to

关键词： hyperspectral HSI atmospheric compensation empirical line method remote sensing climatology

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Spectral BRDF modeling of vehicle signature observations in the VNIR-SWIR 22

Spectral BRDF modeling of vehicle signature observations in ...

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22nd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XXii

作者： Perkins, T. Adler-Golden, S. Muratov, L. Sundberg, R. Ientilucci, E. Cain, L. Spectral Sci Inc 4 Fourth Ave Burlington MA 01803 USA Rochester Inst Technol Digital Imaging & Remote Sensing Lab Rochester NY 14623 USA US Air Force Res Lab RYAT Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510600812

hyperspectral imaging (HSI) sensors have the ability to detect and identify objects within a scene based on the distinct attributes of their surface spectral signatures. Many targets of interest, such as vehicles, represent a complex arrangement of specular (non-Lambertian) materials with curved and flat surfaces oriented at varying view factors. This complexity, combined with possible changing atmospheric/illumination conditions and viewing geometries, can produce significant variations in the observed signatures from measurement to measurement, making detection and/or reacquisition challenging. This paper focuses on the characterization of visible-near infrared-short wave infrared (VNIR-SWIR) spectra for detection, identification and tracking of vehicles. Signature variations are predicted using a novel image simulation tool to calculate spectral images of complex 3D objects from a spectral material description such as the modified Beard-Maxwell BRDF model, a wireframe shape model, and a directional model of the illumination. We compare the simulations with recent VNIR-SWIR hyperspectral imagery of vehicles and panels collected at the Rochester Institute of Technology during an Autumn 2015 measurement campaign. Variations in both the simulated and measured spectra arise mainly from differences in the relative glint contribution. Implications of these variations on vehicle detection and identification are briefly discussed.

关键词： Bidirectional reflectance distribution function target detection hyperspectral imagery reflectance scene simulation

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Identifying vehicles with VNIR-SWIR hyperspectral imagery: Sources of distinguishability and confusion 21

Identifying vehicles with VNIR-SWIR hyperspectral imagery: S...

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SPIE's 21st Imaging Spectrometry conference

作者： Adler-Golden, Steve Sundberg, Robert Spectral Sci Inc 4 Fourth Ave Burlington MA 01803 USA

ISBN: (数字)9781510603448

ISBN: (纸本)9781510603431;9781510603448

multispectral and hyperspectral imaging can facilitate vehicle tracking across a series of images by gathering spectral information that distinguishes the vehicle of interest from confusers. Developing effective algorithms for utilizing this information requires an understanding of the sources and nature of both the common and unique components in vehicle spectra, as well as the variations associated with lighting, view angle, and part of the vehicle being observed. In this study, focusing on the VNIR-SWIR spectral region, we analyze hyperspectral data from a recent field experiment at the Rochester Institute of Technology. We describe the spectra of painted vehicle surfaces in general terms, and demonstrate effective classification of automobiles based on spectra from upward facing surfaces (the roof, hood or trunk) using a method that combines the Support Vector Machine with data pre-conditioning.

关键词： Silver hyperspectral imaging Detection and tracking algorithms Short wave infrared radiation Gold Sensors Sun Algorithm development Light sources and illumination Data processing

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hyperspectral reflectance signature protocol for predicting subsurface bottom reflectance in water: in-situ and analytical methods

Hyperspectral reflectance signature protocol for predicting ...

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conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： Bostater, Charles R., Jr. Rotkiske, Tyler Oney, Taylor Florida Inst Technol Coll Engn Dept Ocean Engn & Sci Marine Environm Opt Lab 150 West Univ Blvd Melbourne FL 32901 USA Florida Inst Technol Ctr Remote Sensing Coll Engn Dept Ocean Engn & Sci 150 West Univ Blvd Melbourne FL 32901 USA

ISBN: (数字)9781510604032

ISBN: (纸本)9781510604025;9781510604032

In-situ measurement of bottom reflectance signatures & bottom features in water are used to test an analytical based irradiance model protocol. Comparisons between predicted and measured bottom reflectance signatures are obtained using measured hyperspectral remote sensing reflectance signatures, water depth and water column constituent concentrations. Analytical solutions and algorithms are used to generate synthetic signatures of different bottom types. The analytical methodology used to simulated bottom reflectance contains offset and bias that can be corrected using spectral window based corrections. Example results are demonstrated for application to coral species, submerged aquatic vegetation and a sand bottom type. Spectral windows are identified for predicting the above bottom types. Sensitivity analysis of predicted bottom reflectance signatures is conducted by varying water depth, chlorophyll, dissolved organic matter and total suspended mater concentrations. The protocol can be applied to shallow subsurface geospatial mapping using sensor based water surface reflectance based upon an analytical model solution derived from primitive radiative transfer theory.

关键词： remote sensing airborne image acquisition hyperspectral remote sensing multispectral imagery shallow subsurface sensing coral reef bottom reflectance sand bottom reflectance seagrass bottom reflectance geospatial mapping application water quality sondes bottom sediment fluid mud satellite imagery fixed platform sensing

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Ellipsoids for Anomaly Detection in Remote Sensing imagery 21

Ellipsoids for Anomaly Detection in Remote Sensing Imagery

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

作者： Grosklos, Guen Theiler, James Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA

ISBN: (纸本)9781628415889

For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large lever-arm outliers and non-Gaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimum-volume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.

关键词： Anomaly Detection multispectral imagery hyperspectral imagery Background Estimation Low False Alarm Rate

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An Analysis Task Comparison of Uncorrected vs. Geo-registered Airborne hyperspectral imagery 21

An Analysis Task Comparison of Uncorrected vs. Geo-registere...

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

作者： Sun, Yihang Kerekes, John Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781628415889

Geo-registration is the task of assigning geospatial coordinates to the pixels of an image and placing them in a geographic coordinate system. However, the process of geo-registration can impair the quality of the image. This paper studies this topic by applying a comparison methodology to uncorrected and geo-registered airborne hyperspectral images obtained from the RIT SHARE 2012 data set. The uncorrected image was analyzed directly as collected by the sensor without being treated, while the geo-registered image was corrected using the nearest neighbor resampling approach. A comparison of performance was done for the analysis tasks of spectral unmixing and subpixel target detection, which can represent a measure of utility. The comparison demonstrates that the geo-registration process can affect the utility of hyperspectral imagery to a limited extent.

关键词： hyperspectral image geo-registration performance comparison unmixing subpixel target detection

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Spatial-Spectral Dimensionality Reduction of hyperspectral imagery with Partial Knowledge of Class Labels 21

Spatial-Spectral Dimensionality Reduction of Hyperspectral I...

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

作者： Cahill, Nathan D. Chew, Selene E. Wenger, Paul S. Rochester Inst Technol Sch Math Sci Ctr Appl & Computat Math Rochester NY 14623 USA

ISBN: (纸本)9781628415889

Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) are effective dimensionality reduction algorithms that are capable of integrating both the spatial and spectral information inherent in a hyperspectral image. In this paper, we consider how to extend LE- and SE-based spatial-spectral dimensionality reduction algorithms to situations where partial knowledge of class labels exists, for example, when a subset of pixels has been manually labeled by an expert user. This partial knowledge is incorporated through the use of cluster potentials, turning each underlying algorithm into an instance of SE. Using publicly available data, we show that incorporating this partial knowledge improves the performance of subsequent classification algorithms.

关键词： Dimensionality reduction Laplacian eigenmaps Schroedinger eigenmaps spatial-spectral fusion

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SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of hyperspectral imagery 21

SLIC Superpixels for Efficient Graph-Based Dimensionality Re...

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

作者： Zhang, Xuewen Chew, Selene E. Xu, Zhenlin Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Ctr Appl & Computat Math Rochester NY 14623 USA

ISBN: (纸本)9781628415889

Nonlinear graph-based dimensionality reduction algorithms such as Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) have been shown to be very effective at yielding low-dimensional representations of hyperspectral image data. However, the steps of graph construction and eigenvector computation required by LE and SE can be prohibitively costly as the number of image pixels grows. In this paper, we propose pre-clustering the hyperspectral image into Simple Linear Iterative Clustering (SLIC) superpixels and then performing LE- or SE-based dimensionality reduction with the superpixels as input. We then investigate how different superpixel size and regularity choices yield trade-offs between improvements in computational efficiency and accuracy of subsequent classification using the low-dimensional representations.

关键词： Superpixels Dimensionality Reduction Laplacian Eigenmaps Schroedinger Eigenmaps Classification

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