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Subpixel target size estimation for remotely sensed imagery

Subpixel target size estimation for remotely sensed imagery

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

作者： Chang, CI Ren, HA D'Amico, F Jensen, JO Univ Maryland Remote Sensing Signal Image Proc Lab Dept Comp Sci Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)0819449539

One of challenges in remote sensing image processing is subpixel detection where the target size is smaller than the ground sampling distance. In this case, targets of interest have their sizes less than the pixel resolution, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected spectrally at subpixel level, not spatially as ordinarily conducted by classical image processing techniques. This paper investigates a more challenging issue than subpixel detection, which is subpixel target size estimation. More specifically, when a single pixel-embedded target is detected, we would like to know what is the size of this particular target within the pixel. Our proposed approach is fully constrained linear spectral unmixing (FCLSU). which allows us to estimate the abundance fraction of the target present in the pixel that determines the size of the target. In order to evaluate the proposed FCLSU, two sets of experiments are conducted, computer simulations and real HYDICE data, where computer simulations are used to plant targets to validate our approach and real data are used to demonstrate the utility of the FCLSU in practical applications.

关键词： Target detection Computer simulations Image processing Error analysis Pixel resolution Remote sensing Scene simulation Data modeling hyperspectral imaging hyperspectral target detection

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The categorization of hyperspectral information (HSI) based on the distribution of spectra in hyperspace

The categorization of hyperspectral information (HSI) based ...

引用

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

作者： Resmini, RG Natl Imagery & Mapping Agcy Reston VA 20191 USA

ISBN: (纸本)0819449539

hyperspectral information (HSI) data are commonly categorized by a description of the dominant physical geographic background captured in the image cube. In other words, HSI categorization is commonly based on a cursory, visual assessment of whether the data are of desert, forest, urban, littoral, jungle, alpine, etc., terrains. Additionally, often the design of HSI collection experiments is based on the acquisition of data of the various backgrounds or of objects of interest within the various terrain types. These data are for assessing and quantifying algorithm performance as well as for algorithm development activities. Here, results of an investigation into the validity of the backgrounds-driven mode of characterizing the diversity of hyperspectral data are presented. HSI data are described quantitatively, in the space where most algorithms operate: n-dimensional (n-D) hyperspace, where n is the number of bands in an HSI data cube. Nineteen metrics designed to probe hyperspace are applied to 14 HYDICE HSI data cubes that represent nine different backgrounds. Each of the 14 sets (one for each HYDICE cube) of 19 metric values was analyzed for clustering. With the present set of data and metrics, there is no clear, unambiguous break-out of metrics based on the nine different geographic backgrounds. The break-outs clump seemingly unrelated data types together;e.g., littoral and urban/residential. Most metrics are normally distributed and indicate no clustering;one metric is one outlier away from normal (i.e., two clusters);and five are comprised of two distributions (i.e., two clusters). Overall, there are three different break-outs that do not correspond to conventional background categories. Implications of these preliminary results are discussed as are recommendations for future work.

关键词： hyperspectral information (HSI) hyperspace HYDICE backgrounds VNIR/SWIR

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Investigation of spectral screening techniques for independent component analysis based hyperspe ctral image processing

Investigation of spectral screening techniques for independe...

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

作者： Robila, SA Univ New Orleans Dept Comp Sci New Orleans LA 70148 USA

ISBN: (纸本)0819449539

This paper investigates the effect of spectral screening on processing hyperspectral data through Independent Component Analysis (ICA). ICA is a multivariate data analysis method producing components that are statistically independent. In the context of the linear mixture model, the endmember abundances can be viewed as independent components, the endmembers forming the columns of the mixing matrix. In essence, the ICA processing can be seen as an alternative solution to endmember unmixing. In the context of feature extraction, each feature will be represented by an independent component, thus leading to maximum separability among features. Spectral screening is defined as the reduction of the image cube to a subset of representative pixel vectors with the goal of achieving a considerable speedup in further processing. At the base of spectral screening are the measure used to assess the similarity between two pixel vectors and a threshold value. Two pixel vectors are similar if the value yielded by the similarity measure is smaller than the threshold and dissimilar otherwise. The spectral screened subset has to be formed such that any two vectors in the subset are dissimilar and for any vector in the original image cube there is a similar vector in the subset. The method we present uses spectral angle as distance measure. A necessary condition for the success of spectral screening is that the result of processing the reduced subset can be extended to the entire data. Intuitively, a larger subset would lead to increased accuracy. However, the overhead introduced by the spectral screening is directly proportional to the subset size. Our investigation has focused on finding the "ideal" threshold value that maximizes both the accuracy and the speedup. The practical effectiveness of the methods was tested on HYDICE data. The results indicate that considerable speedup is obtained without a considerable loss of accuracy. The presented method leads to a significant increase

关键词： spectral screening independent component analysis spectral angle hyperspectral imagery processing feature separability

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Geobotanical remote sensing applied to targeting new geothermal resource locations in the U.S. Basin and range with a focus on Dixie Meadows, Nevada

Geobotanical remote sensing applied to targeting new geother...

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International Collaboration for Geothermal Energy in the Americas - Geothermal Resources Counsil: 2003 Annual Meeting

作者： Pickles, William L. Nash, Gregory D. Calvin, Wendy M. Martini, Brigette A. Cocks, Peter A. Kennedy-Bowdoin, Ty. MacKnight iv, Robert B. Silver, Eli A. Potts, Donald C. Foxall, William Kasameyer, Paul Waibel, Albert F. University of California LLNL University of Utah University of Nevada Reno HyVista Corporation Sydney Australia University of California Santa Cruz Columbia Geoscience

This paper presents an overview of the work our collaboration is doing to increase the detailed mapped resource base for geothermal exploration in the Western US. We are imaging several large areas in the western US with high resolution airborne hyperspectral and satellite multispectral sensors. We have now entered the phase where the remote sensing techniques and tools we are developing are mature enough to be combined with other geothermal exploration techniques such as aeromagnetic, sesmic, well logging and coring data. The imaging sensors and analysis techniques we have developed give us the ability to map visible faults, surface effluents, altered minerals, and subtle hidden faults. Large regions are being imaged at reasonable costs. The technique of geobotanical remote sensing for geothermal signatures is based on recent successes in mapping hidden faults, high temperature altered mineralization, clays, hot and cold springs and CO2 effluents the Long Valley Caldera and Mammoth Mountain in California. The areas that have been imaged include Mammoth Mountain and the Long Valley Caldera, Dixie Meadows NV, Fish Lake Valley NV, and Brady Hot Springs. Areas that are being imaged in the summer of 2003 are the south moat of the Long Valley Caldera, Mammoth Mountain western flanks, Mono Inyo chain north of Mammoth Mountain in CA, and the Humboldt Block in NV. This paper focuses on presenting the overview of the high-resolution airborne hyperspectral image acquisition that was done at Dixie Meadows NV in August 2002. This new imagery is currently being analyzed and combined with other field data by all of the authors on this paper. Results of their work up until June 23, 2003 are presented in other papers in the this volume, and their results up until the time of the conference will be presented in the remote sensing session on Tuesday morning.

关键词： Remote sensing

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Demonstration of the accuracy of improved resolution hyperspectral imagery

Demonstration of the accuracy of improved resolution hypersp...

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

作者： Vrabel, J Doraiswamy, P McMurtrey, J Stern, A Imaging Technol Consultants Centreville VA 20120 USA

ISBN: (纸本)0819444758

Numerous researchers have demonstrated the accuracy and utility of improved spatial resolution multispectral imagery by sharpening it with higher spatial resolution panchromatic imagery. A much more limited number of researchers have sharpened hyperspectral imagery with panchromatic imagery. In this research we have developed an algorithm that spatially sharpens specific ranges of hyperspectral bands with spectrally correlated multispectral bands of a higher spatial resolution to improve the spatial resolution of the hyperspectral imagery while maintaining or improving it's spectral fidelity. Preliminary validation of the algorithm has been conducted using a 7m AVIRIS scene of the Maryland Eastern Shore containing corn, soybean, and wheat fields. This data was used to simulate 28m HSI and 7m MSI that were used in the sharpening process. Initial analysis has verified the spectral accuracy of the sharpened data. In the next phase of the study, airborne spectral data from two different sensors will be used in the sharpening process with the results used as input for USDA/ARS crop yield and stress models.

关键词： fusion sharpening imagery hyperspectral multispectral remote sensing

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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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Restoration, target recognition, and countermeasure removal using the Computed Tomographic Imaging Spectrometer (CTIS)

Restoration, target recognition, and countermeasure removal ...

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

作者： Schau, HC Soulon, RE Hall, GL Garrood, DJ Raytheon Electronic Systems (United States)

Computed Tomographic Imaging Spectrometers are described and shown to be capable of providing real-time flash multispectral imagery. Restoration equations and techniques for optimizing performance are described. Exper... 详细信息

ISBN: (纸本)0819444758

关键词： imaging spectrometer multispectral imagery diffractive optical elements

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Normal compositional models: Generalizations and applications

Normal compositional models: Generalizations and application...

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

作者： Stein, DWJ MIT Lincoln Lab Grp 97 Lexington MA 02420 USA

ISBN: (纸本)0819444758

The normal compositional model (NCM) is a descriptive model that explicitly accounts for sub-pixel mixing and random variation of the spectrum of a material. In this paper the normal compositional model, defined in an earlier work, is extended to include an additive term that may represent path radiance and additive sensor noise. If the covariance matrix of the additive term is non-singular, as may be assumed since it includes the covariance matrix of the additive noise, the covariance matrix of the other classes need not be non-singular. Thus the current model synthesizes the linear unmixing and Gaussian clustering algorithms. Anomaly and matched target detection algorithms based on these three models are compared using ocean hyperspectral imagery, and for these data the NCM approach reduces the false alarm probability by more than an order of magnitude. The linear mixture and normal compositional models separate surface reflections and upwelling light more effectively than the Gaussian clustering algorithm. Furthermore, greater inter-band correlation is estimated using the subpixel covariance estimation methodology than using the pure pixel modeling approach.

关键词： hyperspectral imagery sub-pixel analysis classification linear unmixing detection segmentation

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Further investigations into the use of linear and nonlinear mixing models for hyperspectral image analysis

Further investigations into the use of linear and nonlinear ...

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

作者： Guilfoyle, KJ Althouse, ML Chang, CI Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819444758

This paper presents the latest results of a series of experiments designed to identify situations in which linear and nonlinear mixing models are expected to occur. It continues to investigate the possibility that there may be naturally occurring situations in which the typically used linear mixture model may not provide the most accurate spectrum. It has already been shown that for specific situations, in binary mixing cases, the nonlinear mixing model can produce more accurate endmember abundance estimates. These results are extended to include ternary and quaternary mixtures, as well as hyperspectral imagery collected over Cuprite, Nevada. In order to test these hypotheses, laboratory endmemeber and mixture data are collected in various scenarios for analysis. As shown in experiments, ternary and quaternary mixtures are more complicated than binary mixtures and the nonlinear mixing is more likely to occur in ternary and quaternary mixtures than in binary mixtures.

关键词： hyperspectral imagery linear mixing nonlinear mixing radial basis function neural network

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Two-stage band selection algorithm for hyperspectral imagery

Two-stage band selection algorithm for hyperspectral imagery

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

作者： Vélez-Reyes, M Linares, DM Jiménez, LO Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819444758

This paper presents a two-stage band optimal band selection algorithm for hyperspectral imagery. The algorithm tries to compute the closest subset of bands to the principal components in the sense of having the smallest canonical correlation. The first stage of the algorithm computes and initial guess for the closest bands using matrix-factorization-based band subset selection. The second stage refines the subset of bands using a steepest ascent algorithm. Experimental results using AVIRIS imagery from the Cuprite Mining District are presented.

关键词： hyperspectral imagery dimensionality reduction band selection principal component analysis

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