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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41-50 订阅

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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...

引用

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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Wavelet-based dimension reduction for hyperspectral image classification

Wavelet-based dimension reduction for hyperspectral image cl...

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

作者： Bosch, EH Lin, JE Topog Engn Ctr Engineer Res & Dev Ctr Alexandria VA 22315 USA

ISBN: (纸本)0819449539

For dimension reduction of hyperspectral imagery, we propose a modification to Principal Component Analysis (PCA), Karhunen-Loeve Transform, by choosing a set of basis vectors corresponding to the proposed transformation to be not only orthonormal but also wavelets. Although the eigenvectors of the covariance matrix of PCA minimize the mean square error over all other choices of orthonormal basis vectors, we will show that the proposed set of wavelet basis vectors have several desirable properties. After reducing the dimensionality of the data, we perform a supervised classification of the original and reduced data sets, compare the results, and assess the merits of such transformation.

关键词： hyperspectral imagery dimension reduction wavelet transform and adaptive wavelets classification

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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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Evaluation of Different Structural Models for Target Detection in hyperspectral imagery

Evaluation of Different Structural Models for Target Detecti...

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

作者： Pena-Ortega, Carolina Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)9780819481597

Target detection is an essential component for defense, security and medical applications of hyperspectral imagery. Structured and unstructured models are used to model variability of spectral signatures, for the design of information extraction algorithms. In structured models, spectral variability is modeled using different geometric representations. In linear approaches, the spectral signatures are assumed to be generated by the linear combination of basis vectors. The nature of the basis vectors, and its allowable linear combinations, define different structural models such as vector subspaces, polyhedral cones, and convex hulls. In this paper, we investigate the use of these models to describe background of hyperspectral images, and study the performance of target detection algorithms based on these models. We also study the effect of the model order in the performance of target detection algorithms based on these models. Results show that model order is critical to algorithm performance. Underfitting or overfitting result in poor performance. Models based on subspace are of lower order than those based on polyhedral cones or convex hulls. With good target to background contrast all models perform well.

关键词： hyperspectral imagery Target detection Structured models Endmember extraction

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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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Non-literal pattern recognition method for hyperspectral imagery exploitation using evolutionary computing methods

Non-literal pattern recognition method for hyperspectral ima...

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3rd Spectrometry conference

作者： Burman, JA GTE Government Systems (United States)

ISBN: (纸本)0819425400

multispectral and hyperspectral image sets contain large amounts of data which are difficult to exploit by manual means because they are comprised of multiple bands of image data that are not easily visualized or assessed. Non-literal imagery exploitation refers to a process that exploits non-spatial information by focusing on individual pixel signatures that span the spectral range of the sensor. GTE has developed a system that utilizes Evolutionary Computing techniques as a potential aid to imagery analysts to performs automatic object detection, recognition and materials identification on multispectral and hyperspectral imagery. The system employs sophisticated signature preprocessing and a unique combination of non-parametric search algorithms guided by a model based cost function to achieve rapid convergence and pattern recognition. The system is scaleable and is capable of discriminating decoys from real objects, identifying pertinent materials that comprise a specific object of interest and estimating the percentage of materials present within a pixel of interest (spectral unmixing).

关键词： non-literal multispectral imagery hyperspectral imagery evolutionary computing neural networks genetic algorithms pattern recognition target detection object recognition spectral unmixing

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Ship Detection in MODIS imagery

Ship Detection in MODIS Imagery

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

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00680 USA

ISBN: (纸本)9780819486226

Understanding the capabilities of satellite sensors with spatial and spectral characteristics similar to those of MODIS for Maritime Domain Awareness (MDA) is of importance because of the upcoming NPOES with 100 minutes revisit time carrying the MODIS-like ViiRS multispectral imaging sensor. This paper presents an experimental study of ship detection using MODIS imagery. We study the use of ship signatures such as contaminant plumes in clouds and the spectral contrast between the ship and the sea background for detection. Results show the potential and challenges for such approach in MDA.

关键词： Ship Detection MODIS imagery Target Detection Ship Plumes Maritime Domain Awareness NPOESS

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Realtime Online processing for remote sensing imagery

Realtime Online processing for remote sensing imagery

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

作者： Du, Q Shia, BE Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)0819449539

The realtime implementation is discussed for several detection and classification techniques: Orthogonal Subspace Projection (OSP), Filter Vector Alorithm (FVA), Generalized Likelihood Ratio Test (GLRT), RX algorithm, Constrained Energy Minimization (CEM), Target Constrained Interference Minimization Filter (TCIMF), and Constrained Linear Discriminant Analysis (CLDA). Two data dimensionality limitations are met in realtime processing. One is the number of classes to be classified cannot be larger than data dimensionality, i.e., the number of spectral bands (for some techniques), and the other is the number of independent pixel vectors used for processing must be larger than the number of bands for a data sample correlation or covariance matrix with full rank (for some techniques). In this paper, we present methods to take care of these two limitations: the former is solved by generating artificial band images to expand the data dimensionality, while the latter is solved by using a positive definite correlation matrix as initial matrix. Experiments using hyperspectral data and multispectral data demonstrate the effectiveness of these methods.

关键词： detection classification realtime processing multispectral imagery hyperspectral imagery

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LINES OF COMMUNICATION IN LANDSAT TM imagery

LINES OF COMMUNICATION IN LANDSAT TM IMAGERY

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conference on algorithms for multispectral and hyperspectral imagery

作者： GEE, SJ HUGHES AIRCRAFT CO DEPT IMAGE EXPLOITATEL SEGUNDOCA 90245

ISBN: (纸本)0819415359

Image analysis is especially laborintensive in the multispectral domain due to the non-intuitive nature of the data. The data is frequently separated by frequency range into a set of image planes. For image analysts (lAs) and photo interpreters to derive meaningful condusions from the imagery, the data needs to be fused into a form that is coherent to the human mind. The Lines of Communication Extraction (LOCX) system was designed to merge both spectral and spatial image processing technologies into a user-friendly package that exploits the multispectral aspect of LANDSAT TM imagery without overwhelming the user with the complexities of the data. This paper discusses in detail the system components, the operations concept and user interface, and preliminary processing results. © 1994 SPIE. All rights reserved.

关键词： User interfaces

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hyperspectral and SAR imagery data fusion with positive Boolean function

Hyperspectral and SAR imagery data fusion with positive Bool...

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

作者： Chang, YL Chen, CT Han, CC Fan, KC Chen, KS Chang, JH St Johns & St Marys Inst Tech Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)0819449539

High-dimensional spectral imageries obtained from multispectral, hyperspectral or even ultraspectral bands generally provide complementary characteristics and analyzable information. Synthesis of these data sets into a composite image containing such complementary attributes in accurate registration and congruence would provide truly connected information about land covers for the remote sensing community. In this paper, a novel feature selection algorithm applied to the greedy modular eigenspaces (GME) is proposed to explore a multi-class classification technique using data fused from data gathered by the MODIS/ASTER airborne simulator (MASTER) and the Airborne Synthetic Aperture Radar (AIRSAR) during the Pacrim ii campaign. The proposed approach, based on a synergistic use of these fused data, represents an effective and flexible utility for land cover classifications in earth remote sensing. An optimal positive Boolean function (PBF) based multi-classifier is built by using the labeled samples of these data as the classifier parameters in a supervised training stage. It utilizes the positive and negative sample learning ability of minimum classification error criteria to improve the classification accuracy. It is proved that the proposed method improves the precision of image classification significantly.

关键词： hyperspectral SAR data fusion feature selection positive Boolean function greedy modular eigenspaces minimum classification error

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