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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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A class of detection filters for targets and anomalies in multispectral / hyperspectral imagery

A class of detection filters for targets and anomalies in mu...

引用

2006 conference on Computer Vision and Pattern Recognition Workshops

作者： Robila, Stefan A. Montclair State University RI 301 Montclair NJ 07043 United States

ISBN: (纸本)0769526462

In this paper we present a class of detection filters based on variations of the spectral screening. Spectral screening is a technique used in reducing the multispectral / hyperspectral data to a representative subset of spectra. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. Spectral screening is performed in a sequential manner, at each step, the subset being increased with a spectrum dissimilar from all the spectra already selected. The procedure, using the spectral angle as similarity measure is employed in a variety of algorithms for linear unmixing and data compression. We modified the algorithm such that at the selection step the spectrum with the largest distance from the set is selected. While not introducing additional computational complexity, the Maximum Spectral Screening (MSS) algorithm ensures that the overlap among the representatives is minimized. MSS is further improved by choosing as first spectra the one with the largest distance to the diagonal, thus transforming the screening into a deterministic process. Second, we investigated the alternative approach where at each step the spectra with the smallest distance (but larger than a threshold value) is selected (Minimum Spectral Screening - mSS). The detection filters were obtained as the classification projector matrices based on the spectral subset. The developed algorithms were tested on HYDICE hyperspectral data using the spectral angle and the spectral information divergence. The results indicate that MSS outperforms regular spectral screening detection and mSS. © 2006 IEEE.

关键词： Digital filters

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Signature reduction methods for target detection in multispectral remote sensing imagery

Signature reduction methods for target detection in multispe...

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conference on Chemical and Biological Sensors for Industrial and Environmental Monitoring ii

作者： Ren, Hsuan Fang, Jyh Perng Chang, Yang-Lang Natl Cent Univ Ctr Space & Remote Sensing Res Grad Inst Space Sci Dept Comp Sci & Informat Engn Jhongli Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9780819464767

multispectral sensors are still widely used in satellite remote sensing. They usually have spectral bands less than ten channels. The problem for so few channels is that it can not directly solve linear mixture model by least square unmixing for subpixel target detection. In order for least square approach to be effective, the number of bands must be greater than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such constraint is not an issue for hyperspectral images since they generally have hundreds of bands, however, this may not be true for multispectral images where the number of signatures to be classified might be greater than the number of bands. In order to relax this constraint, we present two signature reduction methods to reduce the number of unknowns, based on signature selection and signature fusion. A SPOT image scene will be used for experiment to demonstrate the performance.

关键词： multispectral least square band number constraint (BNC) signature selection signature fusion

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General algorithm for stray light measurements of remote sensing imagery - art. no. 62910Y

General algorithm for stray light measurements of remote sen...

引用

conference on Optical Systems Degradation, Contamination, and Stray Light - Effects, Measurements, and Control ii

作者： Goforth, Mark A. Goforth Scientific Inc. (United States)

ISBN: (纸本)0819463701

Stray light in an imaging system can be unpredictable and may not originate directly from the field of view of the imaged pixel. Depending on the severity of the stray light, this can adversely affect the quality of remote sensing imagery and be difficult to correct. A significant amount of stray light can be visually noticeable and even distracting to the image analyst. Complicating the matter, stray light that originates from outside the field of view can be highly unpredictable and variable across the image, adversely affecting the results of image processing algorithms developed for automated processing. A new algorithm has been developed in an attempt to estimate the amount of stray light in each remote sensing image. The stray light algorithm estimates an average and maximum amount of stray light for each image, and can output an estimated stray light map. The algorithm can be run in an automated batch processing mode for operational monitoring where the results are placed into a database along with other image quality trending factors. This paper presents results of testing the algorithm on simulated and commercial panchromatic and multispectral imagery.

关键词： stray light remote sensing algorithm multispectral IKONOS Quickbird

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Exploration of methods for estimation of number of endmembers in hyperspectral imagery

Exploration of methods for estimation of number of endmember...

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conference on Chemical and Biological Sensors for Industrial and Environmental Monitoring ii

作者： Wu, Chao-Cheng Liu, Weimin Chang, Chein-, I Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signa & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)9780819464767

An endmember is an idealized, pure signature for a class and provides crucial information for hyperspectral image analysis. Recently, endmember extraction has received considerable attention in hyperspectral imaging due to significantly improved spectral resolution where the likelihood of a hyperspectral image pixel uncovered by a hyperspectral image sensor as an endmember is substantially increased. Many algorithms have been proposed for this purpose. One great challenge in endmember extraction is the determination of number of endmembers, p required for an endmember extraction algorithm (EEA) to generate. Unfortunately, this issue has been overlooked and avoided by making an empirical assumption without justification. However, it has been shown that an appropriate selection of p is critical to success in extracting desired endmembers from image data. This paper explores methods available in the literature that can be used to estimate the value, p. These include the commonly used eigenvalue-based energy method, An Information criterion (AIC), Minimum Description Length (MDL), Gershgorin radii-based method, Signal Subspace Estimation (SSE) and Neyman-Pearson detection method in detection theory. In order to evaluate the effectiveness of these methods, two sets of experiments are conducted for performance analysis. The first set consists of synthetic image-based simulations which allow us to evaluate their performance with a priori knowledge, while the second set comprising of real hyperspectral image experiments which demonstrate utility of these methods in real applications.

关键词： hyperspectral imaging Minerals Image analysis Interference (communication) Detection theory Signal to noise ratio Calcite Image sensors Neodymium Signal detection

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Actual evapotranspiration estimation by means of airborne and satellite remote sensing data

Actual evapotranspiration estimation by means of airborne an...

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conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology ViiI

作者： Ciraolo, Giuseppe D'Urso, Guido Minacapilli, Mario Univ Palermo Dept Hydraul Engn & Environm Applicat I-90133 Palermo Italy Univ Naples Fed II Dept Agr Engn & Agron Naples Italy Univ Palermo Dept Engn & Agroforestal Technol ITAF Palermo Italy

ISBN: (纸本)0819464546

During the last the two decades, the scientific community developed detailed mathematical models for simulating land surface energy fluxes and crop evapotranspiration rates by means of a energy balance approach. These models can be applied in large areas and with a spatial distributed approach using surface brightness temperature and some ancillary data retrieved from satellite/airborne remote sensed imagery. In this paper a district scale application in combination with multispectral (LandaSat 7 TM data) and hyperspectral airborne MIVIS data has been carried out to test the potentialities of two different energy balance models to estimate evapotranspiration fluxes from a set of typical Mediterranean crops (wine, olive, citrus). The impact of different spatial and radiometric resolutions of MIVIS (3m x 3m) and LandSat (60m x 60m) on models-derived fluxes has been investigated to understand the roles and the main conceptual differences between the two models which respectively use a "single-layer" (SEBAL) and a "two-layer"(TS) schematisation.

关键词： actual evapotranspiration spatial resolution surface energy balance

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Projection Pursuit Feature Analysis for Pan-Sharpened multispectral Ikonos imagery

Projection Pursuit Feature Analysis for Pan-Sharpened Multis...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Nilofar Azizi Andrew E. Marble Julian Meng Department of Electrical Engineering University of New Brunswick Fredericton NB Canada

Orbiting multispectral (MS) sensors can facilitate feature discrimination in clutter since natural clutter and man-made objects often differ in the energy they radiate across the electromagnetic spectrum. The projection pursuit technique (PP) has been previously proposed for assessing information content in large multivariate data sets such as hyperspectral (HS) imagery. Although the number of spectral bands is limited in MS data sets, this study investigates the suitability of PP for target detection. PP can highlight different features of interest in an image, improving and simplifying subsequent detection. This study uses two data sets: (1) 4 m MS IKONOS data and (2) pan-sharpened 1 m IKONOS MS imagery created by fusing the 4m MS and the associated 1 m panchromatic image sets. It is shown that PP based on the information divergence index can facilitate detection of certain targets, and emphasize features in MS data. This paves the way for an automated target detection and recognition system based around a PP preprocessing procedure

关键词： Image analysis Object detection Spatial resolution hyperspectral sensors hyperspectral imaging Niobium High-resolution imaging Detection algorithms Image sensors Data mining

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A Class of Detection Filters for Targets and Anomalies in multispectral / hyperspectral imagery

A Class of Detection Filters for Targets and Anomalies in Mu...

引用

IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： S.A. Robila Montclair State University Montclair NJ USA

In this paper we present a class of detection filters based on variations of the spectral screening. Spectral screening is a technique used in reducing the multispectral / hyperspectral data to a representative subset of spectra. The subset is formed such that any two spectra in it are dissimilar and, for any spectrum in the original image cube, there is a similar spectrum in the subset. Spectral screening is performed in a sequential manner, at each step, the subset being increased with a spectrum dissimilar from all the spectra already selected. The procedure, using the spectral angle as similarity measure is employed in a variety of algorithms for linear unmixing and data compression. We modified the algorithm such that at the selection step the spectrum with the largest distance from the set is selected. While not introducing additional computational complexity, the Maximum Spectral Screening (MSS) algorithm ensures that the overlap among the representatives is minimized. MSS is further improved by choosing as first spectra the one with the largest distance to the diagonal, thus transforming the screening into a deterministic process. Second, we investigated the alternative approach where at each step the spectra with the smallest distance (but larger than a threshold value) is selected (Minimum Spectral Screening mSS). The detection filters were obtained as the classification projector matrices based on the spectral subset. The developed algorithms were tested on HYDICE hyperspectral data using the spectral angle and the spectral information divergence. The results indicate that MSS outperforms regular spectral screening detection and mSS.

关键词： Filters hyperspectral imaging Sections Pixel Detectors Data mining USA Councils Computer vision Pattern recognition conferences

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An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization

An algorithm for unsupervised unmixing of hyperspectral imag...

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

作者： Masalmah, YM Vélez-Reyes, M Rosario-Torres, S Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819457914

This paper presents an approach for simultaneous determination of endmembers and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF by formulating it as a nonnegative least squares problem where the cost function is expanded with a penalty term to enforce the sum to one constraint. Preliminary results using simulated and AVIRIS-Cuprite data are presented. These results show the potential of the method to solve the unsupervised unmixing problem.

关键词： hyperspectral imagery spectral unmixing positive matrix factorization

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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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An algorithm for fully constrained abundance estimation in hyperspectral unmixing

An algorithm for fully constrained abundance estimation in h...

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

作者： Rosario-Torres, S Vélez-Reyes, M Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819457914

This paper presents an algorithm for abundance estimation in hyperspectral imagery. The fully constrained abundance estimation problem where the positivity and the sum to less than or equal to one (or sum equal to one) constraints are enforced is solved by reformulating the problem as a least distance (LSD) least squares (LS) problem. The advantage of reformulating the problem as a least distance problem is that the resulting LSD problem can be solved using a duality theory using a nonnegative LS problem (NNLS). The NNLS problem can then be solved using Hanson and Lawson algorithm or one of several multiplicative iterative algorithms presented in the literature. The paper presents the derivation of the algorithm and a comparison to other approaches described in the literature. Application to HYPERION image taken over La Parguera, Puerto Rico is presented.

关键词： unmixing hyperspectral imagery

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