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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31-40 订阅

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Evaluation of Different Structural Models for Target Detection in hyperspectral imagery

Evaluation of Different Structural Models for Target Detecti...

引用

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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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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Enhancement of Flow-Like Structures in hyperspectral imagery using Tensor Nonlinear Anisotropic Diffusion

Enhancement of Flow-Like Structures in Hyperspectral Imagery...

引用

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

作者： Marin-McGee, Maider Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR USA

ISBN: (纸本)9780819486226

Analyzing flow-like patterns in images for image understanding is an active research area but there have been much less attention paid to the process of enhancement of those structures. The completion of interrupted lines or the enhancement of flow-like structures is known as Coherence-Enhancement (CE). In this work, we are studying nonlinear anisotropic diffusion filtering for coherence enhancement. Anisotropic diffusion is commonly used for edge enhancement by inhibiting diffusion in the direction of highest spatial fluctuation. For CE, diffusion is promoted along the direction of lowest spatial fluctuation in a neighborhood thereby taking into account how strongly the local gradient of the structures in the image is biased towards that direction. Results of CE applied multispectral and hyperspectral images are presented.

关键词： hyperspectral Image Processing Nonlinear Diffusion Coherence-Enhancement Image Processing

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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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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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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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Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

Selecting training and test images for optimized anomaly det...

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

作者： Mindrup, Frank M. Friend, Mark A. Bauer, Kenneth W. USAF Inst Technol Wright Patterson AFB OH USA

ISBN: (纸本)9780819486226

There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques have been applied to some of these algorithms in an attempt to choose robust settings capable of operating consistently across a large variety of image scenes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD;these characteristics include the Fisher's score, ratio of target pixels and number of clusters. This paper describes a method for selecting hyperspectral image training and test subsets yielding consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. Several different mathematical models representing the value of a training and test set based on such measures as the D-optimal score and various distance norms are tested in a simulation experiment.

关键词： hyperspectral imagery robust parameter design anomaly detection training/test set selection

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Biased Normalized Cuts for Target Detection in hyperspectral imagery 22

Biased Normalized Cuts for Target Detection in Hyperspectral...

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

作者： Zhang, Xuewen Dorado-Munoz, Leidy P. Messinger, David W. Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Image Comp & Anal Lab Rochester NY 14623 USA

ISBN: (纸本)9781510600812

The Biased Normalized Cuts (BNC) algorithm is a useful technique for detecting targets or objects in RGB imagery. In this paper, we propose modifying BNC for the purpose of target detection in hyperspectral imagery. As opposed to other target detection algorithms that typically encode target information prior to dimensionality reduction, our proposed algorithm encodes target information after dimensionality reduction, enabling a user to detect different targets in interactive mode. To assess the proposed BNC algorithm, we utilize hyperspectral imagery (HSI) from the SHARE 2012 data campaign, and we explore the relationship between the number and the position of expert-provided target labels and the precision/recall of the remaining targets in the scene.

关键词： Biased Normalized Cuts Dimensionality Reduction Laplacian Eigenmaps Schroedinger Eigenmaps Target Detection

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ICA mixture model for unsupervised classification of non-Gaussian classes in multi/hyperspectral imagery

ICA mixture model for unsupervised classification of non-Gau...

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

作者： Shah, CA Arora, MK Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819449539

Conventional remote sensing classification techniques model the data in each class with a multivariate Gaussian distribution. Inadequacy of such algorithms stems from Gaussian distribution assumption for the class-component densities, which is only an assumption rather than a demonstrable property of natural spectral classes. In this paper, we present an Independent Component Analysis (ICA) based approach for unsupervised classification of multi/hyperspectral imagery. ICA employed for a mixture model, estimates the data density in each class and models class distributions with non-Gaussian structure (i.e. leptokurtic or platykurtic p.d.f.), formulating the ICA mixture model (ICAMM). It finds independent components and the mixing matrix for each class, using the extended informationmaximization learning algorithm, and computes the class membership probabilities for each pixel. We apply the ICAMM for unsupervised classification of images from a multispectral sensor - Positive Systems Multi-Spectral Imager, and a hyperspectral sensor - Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Four feature extraction techniques: Principal Component Analysis, Segmented Principal Component Analysis, Orthogonal Subspace Projection and Projection Pursuit have been considered as a preprocessing step to reduce dimensionality of the hyperspectral data. The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of remotely sensed images.

关键词： independent component analysis (ICA) ICA mixture model (ICAMM) blind source separation (BSS) unsupervised classification unsupervised feature extraction multispectral and hyperspectral imagery

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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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