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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery IV"

共 387 条记录，以下是331-340 订阅

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Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery

Shadow-insensitive material detection/classification with at...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Adler-Golden, SM Levine, RY Matthew, MW Richtsmeier, SC Bernstein, LS Gruninger, J Felde, G Hoke, M Anderson, GP Ratkowski, A Spectral Sciences Inc. (United States) Air Force Research Lab. (United States)

ISBN: (纸本)0819440760

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sun and by the sky. Some specific algorithm and applications are illustrated using hyperspectral Digital imagery Collection Experiment (HYDICE) data.

关键词： detection classification hyperspectral image shadow

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Evolving forest fire burn severity classification algorithms for multi-spectral imagery

Evolving forest fire burn severity classification algorithms...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Brumby, SP Harvey, NR Bloch, JJ Theiler, J Perkins, S Young, AC Szymanski, JJ Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819440760

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

关键词： multispectral imagery genetic programming supervised classification forest fire wildfire

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algorithms for multispectral, hyperspectral, and ultraspectral imagery VII

Algorithms for multispectral, hyperspectral, and ultraspectr...

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algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

The proceedings contains 59 papers from the conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII. Topics discussed include: spectral subspace matched filtering;automated optimal channel selection for spectral imaging sensors;band selection for lossless image compression;automated hyper/multispectral image analysis tools;and efficient materials mapping for hyperspectral data.

关键词： Image analysis

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Band selection for lossless image compression

Band selection for lossless image compression

引用

conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

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

ISBN: (纸本)0819440760

Lossless compression algorithms typically do not use spectral prediction, and typical algorithms that do, use only one adjacent band. Using one adjacent band has the disadvantage that if the last band compressed is needed, all previous bands must be decompressed. One way to avoid this is to use a few selected bands to predict the others. Exhaustive searches for band selection have a combinatorial problem, and are therefore not possible except in the simplest cases. To counter this, the use of a fast approximate method for band selection is proposed. The bands selected by this algorithm are a reasonable approximation to the principal components. Results are presented for exhaustive studies using entropy measures, sum of squared errors, and compared to the fast algorithm for simple cases. Also, it was found that using six bands selected by the fast algorithm produces comparable performance to one adjacent band.

关键词： image compression hyperspectral imagery band selection principal component analysis

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Semi-autonomous registration of satellite imagery using feature fitting

Semi-autonomous registration of satellite imagery using feat...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Smith, JL Motomatsu, SE Taylor, JG Jefferson, KJ Stallard, BR Sandia Natl Labs Albuquerque NM 87185 USA

ISBN: (纸本)0819440760

Interband coregistration of multispectral satellite imagery is essential to exploiting the spectral information inherent in these data. A semi-automatic image registration method has been developed for multispectral Thermal Imager (MTI) data. This registration method, based on feature fitting within the image, is applicable to the 14 MTI spectral bands that contain ground information;these spectral bands range from 0.45 to 10.7 mum. The feature fitting registration method requires selection of an appropriate scene feature in the image, usually a crossroad or other feature with moderately high contrast to compute the required shift in x and y for each band. This paper describes the algorithm and provides examples of images registered using this method. Preliminary results show that for MTI image registration, feature fitting yields better results than crosscorrelation. Results also show that this algorithm works well for a broad variety of scenes;this algorithm has been applied to images with scene content ranging from desert images with very little structure to heavily forested images. This method has been developed in support of the MTI mission, but may easily be extended for use on image data collected by other multispectral sensors.

关键词： Image registration Sensors Detection and tracking algorithms Satellites multispectral imaging Long wavelength infrared Satellite imaging Earth observing sensors Image processing Near infrared

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A band sharpening technique for multiresolution spectral data sets using regression residuals.

A band sharpening technique for multiresolution spectral dat...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Lewis, VS DOIAIU Natl Imagery & Mapping Agcy Washington DC 20301 USA

ISBN: (纸本)0819440760

This paper proposes a band sharpening technique for data sets with multiple bands of data at a fine resolution and one or more bands of data at a coarse resolution. A linear prediction model of the coarse resolution data is calculated using the fine resolution data, along with it's associated residual data. A series of smoothing filters was applied to this residual data and added back into the output of the linear predictor for the final result, which was then compared to the original input data with preliminary exploratory analysis. The most effective smoothing filter appears to be a median filter of the order n+1 (with n being the nearest integer to the ratio of coarse resolution to fine resolution data). Initial radiometric comparisons are also presented here.

关键词： band sharpening linear prediction residual median filtering

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A Gibbs-based unsupervised segmentation approach to partitioning hyperspectral imagery for terrain applications

A Gibbs-based unsupervised segmentation approach to partitio...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Rand, RS Keenan, DM USA Erdc Topog Engn Ctr Alexandria VA 22315 USA

ISBN: (纸本)0819440760

A Gibbs-based approach to partitioning hyperspectral imagery into homogeneous regions is investigated for terrain mapping applications. The form of Bayesian estimation, Maximum A Posteriori (MAP) estimation, is applied through the use of a Gibbs distribution defined over a neighborhood system and is implemented as a multi-grid process. Appropriate energy functions and neighborhood graph structures are investigated, which model spectral disparities in an image using spectral angle and/or Euclidean distance. Experiments are conducted on a HYDICE scene collected over an area adjacent to Fort Hood, Texas, that contains a diverse range of terrain features and that is supported with ground truth. Suitable parameter ranges are investigated,and the behavior of the algorithm is characterized using individual and combined measures of disparity within the context of a more general framework, one that supports mixed-pixel processing.

关键词： Gibbs-based algorithm Markov Random Field partitioning clustering spectral mixture analysis hyperspectral imagery

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Unsupervised target subpixel detection in hyperspectral imagery

Unsupervised target subpixel detection in hyperspectral imag...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Chang, CI Du, Q Chiang, SS Heinz, DC Ginsberg, IW Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819440760

Most subpixel detection approaches require either fall or partial prior target knowledge. In many practical applications, such prior knowledge is generally very difficult to obtain, if not impossible. One way to remedy this situation is to obtain target information directly from the image data in an unsupervised manner. In this paper, unsupervised target subpixel detection is considered. Three unsupervised learning algorithms are proposed, which are the unsupervised vector quantization (UVQ) algorithm, unsupervised target generation process (UTGP) and unsupervised NCLS (UNCLS) algorithm. These algorithms produce necessary target information from the image data with no prior information required. Such generated target information is referred to as aposteriori target information and can be used to perform target detection.

关键词： subpixel target detection unsupervised nonnegatively constrained least squares (UNCLS) unsupervised target generation process (UTGP) unsupervised vector quantization (UVQ)

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Quantitative study of detection performance for LWIR hyperspectral imagers as a function of number of spectral bands

Quantitative study of detection performance for LWIR hypersp...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Mayer, R Priest, R USN Res Lab Div Opt Sci Washington DC 20375 USA

ISBN: (纸本)0819440760

Remote passive sensors can collect data that depict both the spatial distribution of objects in the scene and the spectral distributions for those objects within the scene. Target search techniques, such as matched filter algorithms, use highly resolved wavelength spectra (large number of bands) to help detect fine features in the spectrum in order to discriminate objects from the background. The use of a large number of bands during the target search, however, significantly slows image collection and area coverage rates. This study quantitatively examines how binning or integrating bands can affect target detection. Our study examines the long-wave infrared spectra of man-made targets and natural backgrounds obtained with the SEBASS (8-12 mum) imager as part of the Dark HORSE 2 exercise during the HYDRA data collection in November, 1998. In this collection, at least 30 bands of data were obtained, but they were then binned to as few as 2 bands. This study examines the effect on detection performance of reducing the number of bands, through computation of the signal to clutter ratio (SCR) for a variety of target types. In addition, this study examines how band reduction affects the receiver operator curves (ROC) i.e. the target detection probability versus false alarm rate, for matched filter algorithms using in-scene target signatures and hyperspectral images. Target detection, as measured by SCR, for a variety of target types, improves with increasing number of bands. The enhancement in SCR levels off at approximately 10 bands, with only a small increase in SCR obtained from 10 to 30 bands. Variable number of bands within a bin (for fixed number of bins), generated by a genetic algorithm, increases SCR and enhances ROC curve performance for multi-temporal studies. Thus, optimal selection of bands derived from one mission, may be robust and stable, and provide enhanced target detection for data collected on subsequent days. This investigation is confined to the stu

关键词： algorithms for hyperspectral imagery remote sensing genetic algorithm automatic target detection

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Support vector machines for broad area feature classification in remotely sensed images

Support vector machines for broad area feature classificatio...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery VII

作者： Perkins, S Harvey, NR Brumby, SP Lacker, K Los Alamos Natl Lab Space & Remote Sensing Sci Los Alamos NM 87545 USA

ISBN: (纸本)0819440760

Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called AFREET, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to un een data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hyperspectral data), and scales well to large problem sizes. AFREET combines an SVM with a sophisticated spatio-spectral feature construction mechanism that allows it to classify spectrally ambiguous pixels. We demonstrate the effectiveness of the system by applying AFREET to several broad area classification problems in remote sensing, and provide a comparison with conventional maximun likelihood classification.

关键词： support vector machines feature construction supervised learning image feature classification remote sensing multispectral imagery hyperspectral imagery spatial context

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