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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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Using maps to automate the classification of remotely-sensed imagery 2

Using maps to automate the classification of remotely-sensed...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Carlotto, MJ PACIFIC SIERRRA RES CORP ARLINGTONVA 22209

ISBN: (纸本)0819421391

The accurate classification of remotely sensed imagery usually requires some form of ground truth data. Maps are potentially a valuable source of ground truth but have several problems (e.g., they are usually out-dated, features are generalized, and thematic categories in the map often do not correspond to distinct clusters or segments in the imagery). We describe several methods for using maps to automate the classification of remotely sensed data, specifically landsat thematic mapper imagery. In each, map data are co-registered to all or a part of the image to be classified. A probability model relating spectral clusters derived from the imagery to thematic categories contained in the map is then estimated. This model is computed globally and adjusted locally based on context. By computing the probability model over a large area (e.g., the full landsat scene) general relationships between spectral categories and clusters are captured even though there are differences between the image and the map. Then, by adjusting and applying the model locally, new features can be extracted from the image that are not contained in the map and, in certain cases, different classes can be assigned to the same cluster in different parts of the image based on context. Experimental results are presented for several landsat scenes. Several of the methods produced results that were more accurate than the map. We show that these methods are able to enhance the spatial detail of features contained in the map, identify new features not present in the map, and fill in areas in which map coverage does not exist.

关键词： digital cartographic data maps multispectral classification bayesian inference contextual information

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The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

The impact of initialization procedures on unsupervised unmi...

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

作者： Masalmah, Yahya M. Velez-Reyes, Miguel Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc Mayaguez CampusPOB 9042 Mayaguez PR 00681 USA

ISBN: (纸本)9780819466877

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

关键词： hyperspectral imagery spectral unmixing abundance estimation endmember determination iterative methods initialization Positive Matrix Factorization

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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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Developing high resolution clutter for wireless network propagation using Worldview-2 imagery

Developing high resolution clutter for wireless network prop...

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Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XViiI

作者： Gwata, Batsi ComputaMaps Cape Town South Africa

ISBN: (纸本)9780819490681

Technological advancement has lead to an increased demand for up-to-date, high resolution solutions for Wireless Network Propagation. This research paper serves to highlight how 50cm resolution clutter classes can be accurately extracted from WorldView-2 imagery using semi-automated processes. Once the clutter classes have been extracted, a 3D model of the area is created for visualization purposes. The study area for this research was Rustenburg, South Africa.

关键词： Clutter Wireless Network Propagation Worldview-2 imagery

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Restoration of hyperspectral imagery

Restoration of hyperspectral imagery

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

作者： Umana-Diaz, Alejandra Velez-Reyes, Miguel Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc POB 9048 Mayaguez PR 00681 USA

ISBN: (纸本)0819462896

In hyperspectral imaging, the quality of the collected spectral signatures can be degraded by blurring due to the channel weighting function of the imaging spectrometer. In this work, we are investigating reconstruction techniques to enhance salient features and remove degradation effects in measured spectra to assist in subsequent machine analysis. Here preliminary work is presented showing spectral restoration using simulated data and real data of the AVIRIS NW Indian Pines hyperspectral image using different restoration algorithms. The restored AVIRIS image was classified and the classification accuracy was used to assess the usefulness of the restoration process. All the methods gave comparable results with the Jansson method giving slightly higher classification accuracy.

关键词： hyperspectral imaging spectral restoration hyperspectral image restoration

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Seeing through heavily polluted satellite imagery using QUAC 24

Seeing through heavily polluted satellite imagery using QUAC

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

作者： Adler-Golden, Steven Bernstein, Lawrence St Peter, Benjamin Tannian, Bridget Spectral Sci Inc Burlington MA 01803 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Extremely thick haze caused by air pollution is observed in many satellite images of the earth, and in particular over eastern China Standard image display software typically provides satisfactory visualization of the ground through automated or user-driven scaling to enhance contrast;however, it does not perform well with these highly polluted scenes, where the haze is spatially non-uniform. Furthermore, estimation of surface reflectance using standard atmospheric correction software is highly problematic under these conditions due to very low visible transmission of the haze coupled with lack of knowledge of its optical properties, which may not conform to the haze or aerosol models in the software. In this paper we show that a version of the empirical Quick Atmospheric Correction (QUAC) algorithm, adapted for spatially dependent scattering, produces visually satisfying imagery of the entire ground in multispectral satellite scenes containing thick haze, and that the output reflectance spectra appear to be realistic enough for performing basic surface classification. The QUAC algorithm is applicable to multispectral and hyperspectral imagery with any number of wavelength bands, including true color (RGB) imagery, and does not require radiometrically calibrated data.

关键词： atmospheric correction multispectral image haze air pollution Planet Labs QUAC

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Iterative algorithms for unmixing of hyperspectral imagery

Iterative algorithms for unmixing of hyperspectral imagery

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

作者： Vélez-Reyes, M Puetz, A Hoke, P Lockwood, RB Rosario, S Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

This paper addresses the use of multiplicative iterative algorithms to compute the abundances in unmixing of hyperspectral pixels. The advantage of iterative over direct methods is that they allow incorporation of positivity and sum-to-one constraints of the abundances in an easy fashion while also allowing better regularization of the solution for the ill-conditioned case. The derivation of two iterative algorithms based on minimization of least squares and Kulback-Leibler distances are presented. ne resulting algorithms are the same as the ISRA and EMML algorithms presented in the emission tomography literature respectively. We show that the ISRA algorithm and not the EMML algorithm computes the maximum likelihood estimate of the abundances under Gaussian assumptions while the EMML algorithm computes a minimum distance solution based on the Kulback-Leibler generalized distance. In emission tomography, the EMML computes the maximum likelihood estimate of the reconstructed image. We also show that, since the unmixing problem is in general overconstrained and has no solutions, acceleration techniques for the EMML algorithm such as the RBI-EMML will not converge.

关键词： unmixing hyperspectral data iterative algorithms abundance estimation

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Tri-lateral Filter on multispectral imagery for Classification and Segmentation

Tri-lateral Filter on Multispectral Imagery for Classificati...

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

作者： Sun, Weihua Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9780819486226

In this paper, we present a new approach to filtering high spatial resolution multispectral (MSI) or hyperspectral imagery (HSI) for the purpose of classification and segmentation. Our approach is inspired by the bilateral filtering method(1) that smooths images while preserving important edges for gray-scale and color images. To achieve a similar goal for MSI/HSI, we build a nonlinear tri-lateral filter that takes into account both spatial and spectral similarities. Our approach works on a pixel by pixel basis;the spectrum of each pixel in the filtered image is the combination of the spectra of its adjacent pixels in the original image weighted by the three factors: geometric closeness, spectral Euclidean distance and spectral angle separation. The approach reduces small clutter across the image while keeping edges with strong contrast. The improvement of our method is that we use both spectral intensity differences together with spectral angle separation as the closeness metric, thus preserving edges caused both by material as well as by similar materials with intensity differences. A k-means classifier is applied to the filtered image and the results show our approach can produce a much less cluttered class map. Results will be shown using imagery from the Digital Globe Worldview-2 multispectral sensor and the HYDICE hyperspectral sensor. This approach could also be expanded to facilitate feature extraction from MSI/HSI.

关键词： Image filtering Optical filters Image segmentation Nonlinear filtering Image classification Sensors Buildings Image processing multispectral imaging Vegetation

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hyperspectral target detection using Independent Component Analysis based linear mixture model

Hyperspectral target detection using Independent Component A...

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

作者： Sarigul, E. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466877

This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to discriminate statistically independent sources to determine end-members in a given dataset as well as their corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that correspond to the target class. The performance of the developed algorithm has been evaluated with several real life hyperspectral image datasets.

关键词： Independent Component Analysis target detection hyperspectral imagery spectral angle mapping unmixing hyperspectral dataindependent

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A full algorithm to compute the constrained positive matrix factorization and its application in unsupervised unmixing of hyperspectral imagery

A full algorithm to compute the constrained positive matrix ...

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

作者： Masalmah, Yahya M. Velez-Reyes, Miguel Univ Turabo Dept Elect & Comp Engn POB 3030 Gurabo PR 00778 USA Univ Puerto Rico Ctr Subsurface Sensing & Imaging Syst Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)9780819471574

This paper presents a full algorithm to compute the solution for the unsupervised unmixing problem based on the positive matrix factorization. The algorithm estimates the number of endmembers as the rank of the matrix. The algorithm has an initialization stage using the SVD subset selection algorithm. Testing and validation with real and simulated data show, the effectiveness of the method. Application of the approach to environmental remote sensing is shown.

关键词： hyperspectral imagery spectral unmixing abundance estimation endmember determination iterative methods initialization positive matrix factorization

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