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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71-80 订阅

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Pigment Diversity Estimation for hyperspectral Images of the Selden Map of China 24

Pigment Diversity Estimation for Hyperspectral Images of the...

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

作者： Bai, Di Messinger, David W. Howell, David Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Univ Oxford Bodleian Lib Broad St Oxford OX1 3BG England

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

The Selden Map of China, an early 17th century wall map of East Asia was rediscovered in 2008. Recently, the map was imaged using a VNIR hyperspectral imaging system while in the collection at the Bodleian Library, Oxford University. The goal of the collection of the hyperspectral image (HSI) of the Selden Map was to help historians understand the material diversity of its composition and potentially the methods used in the creation of the map. The Selden map has been named one of the "Treasures of the Bodleian" and it poses many questions such as the diversity of pigments used to create the map. In this research, we extract visually common pixels (here, the green pigments) from the Selden Map and estimate the material diversity of the green pixels. Previous pigment analysis on the HSI of the Gough Map(1,2) used an endmember based approach, the Gram Matrix technique,(3,4) to understand the number of distinct materials in a scene and then used spectral angle mapper (SAM) to classify all the pigments. Here, we use the same Gram Matrix technique, but due to the complexity of the Selden Map data, instead of using SAM, we use two spectral unmixing techniques, NNLS (nonnegative linear least squares)(5) and FUMI (functions of multiple instances)(6) to determine the weights of all the endmembers for each data point to study the within-material diversity. Results show that the Selden Map is composed of at least 6 kinds of dominant green pigments with a particular spatial pattern. This research provides a useful tool for historical geographers and cartographic historians to analyze the material diversity of HSI of cultural heritage artifacts.

关键词： hyperspectral imagery pigment analysis dimensionality estimation spectral unmixing

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Simulation Techniques for Image Utility Analysis 24

Simulation Techniques for Image Utility Analysis

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

作者： Han, Sanghui Kerekes, John Higbee, Shawn Siegel, Lawrence Chester F Carlson Ctr Imaging Sci Digital Imaging Remote Sensing Lab Rochester Inst Technol Rochester NY 14623 USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Modeling a space borne imaging system is key in predicting mission utility and exploring the sensor design trade space. It is important to capture critical real world phenomena in the modeling as accurately as possible to optimize design parameters. As a step toward optimal design of spectral imaging systems, this work presents simulation techniques that were used to model a panchromatic imaging system and predict well-known image quality metrics for a range of values of a key optical design variable - the effective focal length (EFL). We designed a flat desert scene that included 7% and 15% reflectance panels and generated simulated images for a range of EFLs. The panels were used to calculate a sensor signal-to-noise ratio (SNR). The simulation incorporated a summer atmosphere with a collection time and geometry set to produce zenith solar and nadir collection angles. Platform motion and height with a given integration time for known detector parameters were also incorporated to produce images with changing EFL. A point spread function (PSF) of a typical optical system was incorporated that was scaled according to the EFL for a constant aperture diameter in order to capture the optical resolution changes. The PSF along with smear from the platform motion and integration time introduced realistic image blur to enable the relative edge response (RER) for the system to be estimated from the simulated images. The simulation used the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model that incorporates ray-tracing techniques and physics based radiation propagation modules. The SNR, RER, and GSD calculated from the synthetic images were used to estimate the National imagery Interpretability Rating Scale (NiiRS) rating for a range of EFLs. An optimum EFL was found by this process which included the compensating effects of resolution, sampling, and noise as the EFL changed. This technique is currently being expanded to assess trades for sensor design in ord

关键词： Remote Sensing Simulation Synthetic imagery Image Quality Prediction

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hyperspectral SUPER-RESOLUTION VIA COUPLED TENSOR FACTORIZATION: IDENTIFIABILITY AND algorithms

HYPERSPECTRAL SUPER-RESOLUTION VIA COUPLED TENSOR FACTORIZAT...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Charilaos I. Kanatsoulis Xiao Fu Nicholas D. Sidiropoulos Wing-Kin Ma Department of ECE University of Minnesota Minneapolis MN USA School of EECS Oregon State University Corvallis OR USA *Department of ECE University of Virginia Charlottesville VA USA Department of EE The Chinese University of Hong Kong Shatin NT Hong Kong

ISBN: (纸本)9781538646595

This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the matricized HSI and MSI. This framework is effective to some extent, but several challenges remain. First, it is unclear whether or not the super-resolution image is identifiable in theory under this framework, while identifiability usually plays an essential role in such estimation problems. Second, most algorithms assume that the degradation operators from the super-resolution image to the HSI and MSI are known or can be easily estimated - which is hardly true in practice. In this work, we propose a novel coupled tensor decomposition method that can effectively circumvent these issues. The proposed approach guarantees the identifiability of the super-resolution image under realistic conditions. The method can work even without knowing the spatial degradation operator, which could be hard to accurately estimate in practice. Simulations using AVIRIS Cuprite data are employed to demonstrate the effectiveness of the proposed approach.

关键词： hyperspectral imaging multispectral imaging super-resolution image fusion tensor decomposition identifiability super-resolution Mathematic Identifiability image resolution multispectral imagery image fusion hyperspectral imagery AKR1B10 gene algorithms factorization Tensor Spectral resolution

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Local background estimation and the replacement target model 23

Local background estimation and the replacement target model

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Theiler, James Ziemann, Amanda Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

We investigate the detection of opaque targets in cluttered multi/hyper-spectral imagery, using a local background estimation model. Unlike transparent "additive-model" targets (like gas-phase plumes), these are solid "replacement-model" targets, which means that the observed spectrum is a linear combination of the target signature and the background signature. Pixels with stronger targets are associated with correspondingly weaker backgrounds, and background estimators can over-estimate the background in a target pixel. In particular, "subtracting the background" (which generalizes the usual notion of subtracting the mean) to produce a residual image can actually have deleterious effect. We examine an adaptive partial background subtraction scheme, and evaluate its utility for the detection of replacement-model targets.

关键词： hyperspectral imagery target detection background estimation residual

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Globally scalable generation of high-resolution land cover from multispectral imagery 23

Globally scalable generation of high-resolution land cover f...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Stutts, S. Craig Raskob, Benjamin L. Wenger, Eric J. Appl Res Associates Southeast Div 8537 Six Forks RdSuite 600 Raleigh NC 27615 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

We present an automated method of generating high resolution (similar to 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).

关键词： Big data multispectral imagery machine learning neural networks land cover generation open source data cloud computing

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Spatial-spectral signature modeling for solid targets in hyperspectral imagery 23

Spatial-spectral signature modeling for solid targets in hyp...

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23rd 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 US Air Force Res Lab 2241 Avion Circle Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

Spatial-spectral feature extraction algorithms - such as those based on spatial descriptors applied to selected spectral bands within a hyperspectral image - can provide additional discrimination capability beyond traditional spectral-only approaches. However, when attempting to detect a target with such algorithms, an exemplar target signature is often manually derived from the hyperspectral images representation in the spatial-spectral feature space. This requires a reference image in which the targets location is known. Additionally, the scene based signature captures only the representation of the target under certain collection conditions from a specific sensor, namely, illumination level and atmospheric composition, look angle, and target pose against a specific background. A detection algorithm utilizing this spatial-spectral signature (or the spatial descriptor itself) that is sensitive to changes in these collection conditions could suffer a loss in performance should the new conditions significantly deviate from the exemplars case. To begin to overcome these limitations, we formulate and evaluate the effectiveness of a modeling technique for synthesizing exemplar spatial-spectral signatures for solid targets, particularly when the spatial structure of the target of interest varies due to pose or obscuration by the background, and when applicable, the target temperature varies. We assess the impact of these changes on a group of spatial descriptors responses to guide the modeling process for a set of two-dimensional targets specifically designed for this study. The sources of variability that most affect each descriptor are captured in target subspaces, which then form the basis of new spatial-spectral target detection algorithms.

关键词： hyperspectral imagery spatial-spectral feature extraction spatial-spectral signature modeling spatial-spectral target detection

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Terrestrial hyperspectral image shadow restoration through fusion with terrestrial lidar 23

Terrestrial hyperspectral image shadow restoration through f...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Hartzell, Preston J. Glennie, Craig L. Finnegan, David C. Hauser, Darren L. Univ Houston Dept Civil & Environm Engn 4726 Calhoun Rd Houston TX 77204 USA Univ Houston Natl Ctr Airborne Laser Mapping 5000 Gulf Fwy Houston TX 77204 USA US Army Corps Engineers Cold Reg Res & Engn Lab 72 Lyme Rd Hanover NH 03755 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from exclusively airborne observations to include terrestrial modalities. In contrast to airborne collection geometry, hyperspectral imagery captured from terrestrial cameras is prone to extensive solar shadowing on vertical surfaces leading to reductions in pixel classification accuracies or outright removal of shadowed areas from subsequent analysis tasks. We demonstrate the use of lidar spatial information for sub-pixel HSI shadow detection and the restoration of shadowed pixel spectra via empirical methods that utilize sunlit and shadowed pixels of similar material composition. We examine the effectiveness of radiometrically calibrated lidar intensity in identifying these similar materials in sun and shade conditions and further evaluate a restoration technique that leverages ratios derived from the overlapping lidar laser and HSI wavelengths. Simulations of multiple lidar wavelengths, i.e., multi-spectral lidar, indicate the potential for HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance of shadowed HSI pixels is quantified for imagery of a geologic outcrop through improvements in spectral shape, spectral scale, and HSI band correlation.

关键词： Lidar hyperspectral imagery shadow detection shadow restoration data fusion

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Crop classification using temporal stacks of multispectral satellite imagery 23

Crop classification using temporal stacks of multispectral s...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Moody, Daniela I. Brumby, Steven P. Chartrand, Rick Keisler, Ryan Longbotham, Nathan Mertes, Carly Skillman, Samuel W. Warren, Michael S. Descartes Labs Inc Los Alamos NM 87544 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

关键词： multispectral classification crop classification temporal imagery stacks Landsat 8 classification normalized band difference index classification precision agriculture.

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EVALUATION OF DIMENSIONALITY REDUCTION TECHNIQUES IN hyperspectral imagery AND THEIR APPLICATION FOR THE CLASSIFICATION OF TERRESTRIAL ECOSYSTEMS 23

EVALUATION OF DIMENSIONALITY REDUCTION TECHNIQUES IN HYPERSP...

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conference on Image and Signal Processing for Remote Sensing XXiiI

作者： Ibarrola-Ulzurrun, Edurne Marcello, Javier Gonzalo-Martin, Consuelo ULPGC Inst Oceanog & Cambio Global IOCAG Parque Cient Tecnol Marino Taliarte S-N Telde 35214 Las Palmas Spain Univ Politecn Madrid Ctr Biomed Technol Campus Montegancedo Madrid 28223 Spain

ISBN: (数字)9781510613195

ISBN: (纸本)9781510613195;9781510613188

The hyperspectral imagery is formed by a several narrow and continuous bands covering different regions of the electromagnetic spectrum, such as spectral bands of the visible, near infrared and far infrared. hyperspectral imagery provides extremely higher spectral resolution than high spatial resolution multispectral imagery, improving the detection capability of terrestrial objects. The greatest difficulty found in the hyperspectral processing is the high dimensionality of these data, which brings out the 'Hughes' phenomenon. This phenomenon specifies that the size of training set required for a given classification increases exponentially with the number of spectral bands. Therefore, the dimensionality of the hyperspectral data is an important drawback when applying traditional classification or pattern recognition approaches to this hyperspectral imagery. In our context, the dimensionality reduction is necessary to obtain accurate thematic maps of natural protected areas. Dimensionality reduction can be divided into the feature-selection algorithms and feature-extraction algorithms. We focus the study in the feature-extraction algorithms like Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA). After a review of the state-of-art, it has been observed a lack of a comparative study on the techniques used in the hyperspectral imagery dimensionality reduction. In this context, our objective was to perform a comparative study of the traditional techniques of dimensionality reduction (PCA, MNF and ICA) to evaluate their performance in the classification of high spatial resolution imagery of the CASI (Compact Airborne Spectrographic Imager) sensor.

关键词： hyperspectral imaging Image classification Independent component analysis Principal component analysis Detection and tracking algorithms Ecosystems Sensors Spatial resolution Electromagnetism Remote sensing

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Ensemble Learning and Model Averaging for Material Identification in hyperspectral imagery 23

Ensemble Learning and Model Averaging for Material Identific...

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23rd SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Basener, William F. Univ Virginia Spectral Solut Fac Charlottesville VA 22903 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

In this paper we present a method for identifying the material contained in a pixel or region of pixels in a hyperspectral image. An identification process can be performed on a spectrum from an image from pixels that has been pre-determined to be of interest, generally comparing the spectrum from the image to spectra in an identification library. The metric for comparison used in this paper a Bayesian probability for each material. This probability can be computed either from Bayes' theorem applied to normal distributions for each library spectrum or using model averaging. Using probabilities has the advantage that the probabilities can be summed over spectra for any material class to obtain a class probability. For example, the probability that the spectrum of interest is a fabric is equal to the sum of all probabilities for fabric spectra in the library. We can do the same to determine the probability for a specific type of fabric, or any level of specificity contained in our library. Probabilities not only tell us which material is most likely, the tell us how confident we can be in the material presence;a probability close to 1 indicates near certainty of the presence of a material in the given class, and a probability close to 0.5 indicates that we cannot know if the material is present at the given level of specificity. This is much more informative than a detection score from a target detection algorithm or a label from a classification algorithm. In this paper we present results in the form of a hierarchical tree with probabilities for each node. We use Forest Radiance imagery with 159 bands.

关键词： hyperspectral remote sensing target detection ATR material identification Bayesian Information BIC machine learning model averaging ensemble learning

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