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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51-60 订阅

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Spectral/spatial annealing of hyperspectral imagery initialized by a supervised classification method

Spectral/spatial annealing of hyperspectral imagery initiali...

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

作者： Rand, RS USA Engineer Res & Dev Ctr Alexandria VA 22315 USA

ISBN: (纸本)0819444758

A simulated annealing method of partitioning hyperspectral imagery, initialized by a supervised classification method, is investigated to provide spatially smooth class labeling for terrain mapping applications. The method is used to obtain an estimate of the mode a Gibbs distribution defined over a symmetric spatial neighborhood system that is based on an energy function characterizing spectral disparities in Euclidean distance and spectral angle. Experiments are conducted on a 210-band HYDICE scene that contains a diverse range of terrain features and that is supported with ground truth. Both visual and quantitative results demonstrate a clear benefit of this method as compared to spectral-only supervised classification or unsupervised annealing that has been initialized randomly.

关键词： Gibbs distribution Bayesian Markov Random Field supervised classification Euclidean distance Spectral Angle clustering hyperspectral imagery

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Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery

Characterizing non-Gaussian clutter and detecting weak gaseo...

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

作者： Theiler, J Foy, BR Fraser, AM Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819457914

To detect weak signals on cluttered backgrounds in high dimensional spaces (such as gaseous plumes in hyperspectral imagery) without excessive false alarms requires that the background clutter be effectively characterized. If the clutter is Gaussian, the well-known linear matched filter optimizes the sensitivity to a given plume signal while suppressing the effect of the background clutter. In practice, the background clutter is rarely Gaussian. Here we illustrate non-linear corrections to the matched filter that are optimal for two non-Gaussian clutter models and we report on parametric and nonparametric characterizations of background clutter.

关键词： imagery hyperspectral clutter non-Gaussian matched filter chemical plume

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Atmospheric correction with haze removal including a haze/clear transition region 2

Atmospheric correction with haze removal including a haze/cl...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Richter, R DLR GERMAN AEROSP RES ESTABINST OPTOELECTD-82234 WESSELINGGERMANY

ISBN: (纸本)0819421391

High spatial resolution satellite imagery like Landsat TM and SPOT HRV often contain large regions covered with haze. By applying suitable thresholds clear areas as well as haze and cloud regions can be separated and stored as binary images. Cloud areas are identified in the method presented here, but will not be included in the haze correction, since the ground information cannot be retrieved in optically thick regions. For each spectral band, the haze removal algorithm matches the histogram of the haze areas to the histogram of the clear part of the scene. Due to the binary nature of thresholding this algorithm often causes sharply defined edges at the borders of haze regions. Therefore, a haze boundary region is introduced to generate a smoother transition from haze to clear areas. Two transition methods with a leveled boundary are presented: with fixed weighting and with histogram-dependent weighting. Additionally, the image is atmospherically corrected to remove the remaining atmospheric influence and obtain ground reflectance data.

关键词： atmospheric correction haze removal Landsat TM and SPOT HRV imagery ATCOR2 model

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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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Robust spatial and spectral feature extraction for multispectral and hyperspectral imagery

Robust spatial and spectral feature extraction for multispec...

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conference on algorithms for multispectral and hyperspectral imagery IV

作者： Pinzon, JE Ustin, SL Castaneda, CM Pierce, JF Costick, LA Univ Calif Davis Dept Math Davis CA 95616 USA

ISBN: (纸本)0819428213

We present a hierarchical classification technique that discriminates broad categories of surface materials in terms of ground true features, such as water, vegetation, and soils from spectral information. Subsequently, we further discriminate these materials and extract finer ground features, like chemistries, peculiar to each. The interaction at various scales of the 3D spatial and the spectral domains is decomposed by using wavelet tools to address scale dependencies in the spatial domain, a robust spectral unmixing technique, called Hierarchical Foreground Background Analysis (HFBA) along the spectral axis. HFBA sequentially derives a series of weighting vectors discriminating features at different levels of detection: (1) constituent materials, (2) types within constituents, and (3) chemistries peculiar to each type. Our goal is two-fold. First, we present the combination of HFBA and wavelets as a supervised classification technique validating the categories imposed by the supervised classification, and manifesting clusters which can refine the classification at different scales. Second, we identify spectral redundancies between hyperspectral and multispectral information, studying mixtures at different spatial/spectral resolutions and assess whether targeted features may be extracted as efficiently from multispectral data as they could be from hyperspectral data. Results on AVIRIS and simulated MODIS data illustrate the robustness and effectivity of the technique.

关键词： supervised classification multispectral hyperspectral imagery wavelets non-linear mixture analysis

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Detection in hyperspectral imagery

Detection in hyperspectral imagery

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conference on algorithms for multispectral and hyperspectral imagery IV

作者： Schweizer, SM Moura, JMF Carnegie Mellon Univ ECE Dept Pittsburgh PA 15213 USA

ISBN: (纸本)0819428213

hyperspectral sensors collect hundreds of images in contiguous and narrowly spaced spectral bands. They have the potential to simultaneously provide high spatial and spectral resolution of targets of interest in Automatic Target Detection and Recognition (ATD/R). The price to be paid is the need to process and store an extremely large amount of data in an effective and timely manner. We develop a new implementation of the maximum-likelihood (ML) detector which is both practical and efficient. Our detection is based on a Gauss-Markov Random Field (GMRF) model for the data which avoids the inversion of large data covariance matrices usually encountered in ML-detectors. The paper presents two algorithms to fit the GMRF to the hyperspectral sensor data: an optimal ML estimation algorithm and a suboptimal Least Squares (LS) estimation algorithm. Using the LS-algorithm, we develop the structure of the detector and present estimation results from a real hyperspectral data set.

关键词： hyperspectral Gauss-Markov random field maximum-likelihood detector least squares estimation

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Multi-resolution hyperspectral collection for analysis of panchromatic sharpening algorithms 27

Multi-resolution hyperspectral collection for analysis of pa...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVii

作者： Carrock, Joseph A., ii Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Assessment of panchromatic sharpening algorithms typically starts with a high resolution hyperspectral Image (HSI), which is then spatially degraded so that after the sharpening process, the result can be compared to the original and analyzed for accuracy. This leads to questions about quantitative assessments based solely on simulated low resolution data. To address this problem, a multi-resolution hyperspectral data set was collected by researchers from the Rochester Institute of Technology (RIT) in Henrietta, NY on July 24th 2020. imagery of 48 felt targets, ranging in size from 5 cm to 30 cm and in six different colors, was collected using RIT's MX-1 UAS imaging system, which is designed to collect 272 spectral bands from approximately 400nm to 1000nm. Three flights were performed and images of the target scene were collected at flight altitudes of 30m, 60m, and 120m. The resulting imagery possesses ratios of 2:1 and 4:1 spatial resolution relative to the lowest altitude flight. The goal of this imaging campaign was to create a data set that will be used to test hyperspectral pansharpening algorithms currently under development at RIT. The radiometric accuracy of sharpening algorithms can be better ascertained through quantitative analysis of their results after being applied to this non-simulated multi-resolution hyperspectral data set. This presentation will summarize the process of planning, creating, collecting and packaging the dataset which will be provided to RIT researchers and will also available for download online through RIT.

关键词： panchromatic sharpening hyperspectral imagery multisensor fusion

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Underwater target detection with hyperspectral imagery for search and rescue missions 24

Underwater target detection with hyperspectral imagery for s...

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

作者： Eken, Isa Cem Cetin, Yasemin Yardimci Middle East Tech Univ Dumlupinar Blvd 1 TR-06800 Cankaya Turkey

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Fast and precise target detection is an issue in underwater object detection applications such as search and rescue missions. hyperspectral imaging could be effective in determining different elements in a region thanks to its ability to provide detailed spectral information. Utilization of this rich spectral information calls for the analysis of spectral differences of objects on water surface (dry/wet) and underwater conditions. In our study, we investigated the effect of water column to the target spectrum with two water column correction models and evaluated the results obtained with these models in two detection algorithms. The results show that the methodology enhances underwater object detection performance. The materials used for the study are selected to be typical in search and rescue missions such as metal, cotton fabric with different colors and blue denim to evaluate the effect of these cases on the detectability of targets for such scenarios.

关键词： hyperspectral Underwater Target Detection Search and Rescue

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Subsurface Unmixing for Benthic Habitat Mapping using hyperspectral imagery and Lidar-Derived Bathymetry

Subsurface Unmixing for Benthic Habitat Mapping using Hypers...

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

作者： Torres-Madronero, Maria C. Velez-Reyes, Miguel Goodman, James A. Inst Tecnol Metropolitano Calle 73 76A-354 Via Volador Medellin Colombia Univ Texas El Paso El Paso TX 79968 USA Univ Puerto Rico Puerto Rico PR 00681 USA

ISBN: (纸本)9781628410259

Mapping of benthic habitats from hyperspectral imagery can be achieved by integrating bio-optical models with common techniques for hyperspectral image processing, such as spectral unmixing. Several algorithms have been described in the literature to compensate or remove the effects of the water column and extract information about the benthic habitat characteristics utilizing only measured hyperspectral imagery as input. More recently, the increasing availability of lidar-derived bathymetry information offers the possibility to incorporate this data into existing algorithms, thereby reducing the number of unknowns in the problem, for the improved retrieval of benthic habitat properties. This study demonstrates how bathymetry information improves the mapping of benthic habitats using two algorithms that combine bio-optical models with linear spectral unmixing. hyperspectral data, both simulated and measured, in-situ spectral data, and lidar-derived bathymetry data are used for the analysis. The simulated data is used to study the capabilities of the selected algorithm to improve estimates of benthic habitat composition by combining bathymetry data with the hyperspectral imagery. hyperspectral images captured over Enrique in Puerto Rico using an AISA Eagle sensor is used to further test the algorithms using real data. Results from analyzing this imagery demonstrate increased agreement between algorithm output and existing habitat maps and ground truth when bathymetry data is used jointly with hyperspectral imagery.

关键词： hyperspectral imagery coastal remote sensing spectral unmixing benthic habitat mapping data fusion bathymetric lidar

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