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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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Evaluation of onboard hyperspectral-image compression techniques for a parallel push-broom sensor 2

Evaluation of onboard hyperspectral-image compression techni...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Briles, S LOS ALAMOS NATL LAB GRP NIS 3LOS ALAMOSNM 87545

ISBN: (纸本)0819421391

A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation-coefficient error. Implementation issues are considered in algorithm development.

关键词： hyperspectral imagery image compression wavelets

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Automated map generation and update from high resolution multispectral imagery 2

Automated map generation and update from high resolution mul...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Bullock, ME Fairchild, SR Patterson, TJ Haxton, RS BOOZ ALLEN & HAMILTON INC COLORADO SPRINGSCO 80910

ISBN: (纸本)0819421391

This paper describes the LOCATE TNG system, which generates map products directly from multispectral imagery in an automated fashion. The LOCATE TNG system uses spectral and spatial feature information to extract various types of man- made lines of communication (LOCs) from imagery and generate them in the form of digital vector maps. The generated maps may be compared against reference digital maps to automatically find new or changed LOCs. The original LOCATE (lines of communication apparent from thematic mapper evidence) system was designed and developed to use landsat thematic mapper imagery having a resolution of 30 m. LOCATE TNG (the next generation) has been redesigned to also have the capability to use high resolution multispectral imagery to be available from the next generation of commercial satellites. These satellites will provide multispectral and panchromatic imagery having resolutions down to 4 m and 1 m, respectively, thus dramatically improving the information available for exploitation. LOCATE TNG employs a hierarchical algorithmic approach to extracting layers of LOCs (primary roads, secondary roads, etc.) that may be used for GIS applications.

关键词： multispectral mapping lines of communication automated feature extraction

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Cross-sensor resolution enhancement of hyperspectral images using wavelet decomposition 2

Cross-sensor resolution enhancement of hyperspectral images ...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Peytavin, L MATRA CAP SYST LAB TRAITEMENT IMAGES & SIGNALVELIZY VILLACOUBLFRANCE

ISBN: (纸本)0819421391

In the satellite remote-sensing domain, some technological and physical constraints are not in favor of acquiring high spatial resolution hyperspectral images when there is a need to use high resolution panchromatic images as complementary data to the hyperspectral images. If the data are taken nearly at the same time, some cross-sensor resolution enhancement techniques are able to produce a merged image as close as possible of what would be a high spatial resolution hyperspectral image. Multiresolution wavelet decomposition is the most interesting tool to perform this process. Some merging experiments on AVIRIS images (pixel size 20 meters) and USGS digital aerial images (pixel size 1 meter) have been conducted using this technique. They produced simulated AVIRIS images having a spatial resolution of 5 meters. If the preliminary co-registration step remains critical, global spectral statistics and visual qualitative experimentations show that the result is a non-disturbed hyperspectral image, good candidate for straight exploitation.

关键词： resolution enhancement multiscale wavelet decomposition data fusion hyperspectral imagery

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Nonlinear mean-square estimation with applications in remote sensing 2

Nonlinear mean-square estimation with applications in remote...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Carlotto, MJ PACIFIC SIERRA RES CORP ARLINGTONVA 22209

ISBN: (纸本)0819421391

An approach to image modeling based on nonlinear mean-square estimation that does not assume a functional form for the model is described. The relationship between input and output images is represented in the form of a lookup table that can be efficiently computed from, and applied to images. Three applications are presented to illustrate the utility of the technique in remote sensing. The first illustrates how the method can be used to estimate the values of physical parameters from imagery. Specifically we estimate the topographic component (i.e., the variation in brightness caused by the shape of the surface) from multispectral imagery. The second application is a nonlinear change detection algorithm which predicts one image as a nonlinear function of another. In cases where the frequency of change is large (e.g., due to atmospheric and environmental differences), the algorithm is shown to be superior in performance to linear change detection. In the last application, a technique for removing wavelength- dependent space-varying haze from multispectral imagery is presented. The technique uses the IR bands, which are not affected significantly by haze, to predict the visible bands. Results show a significant reduction in haze over the area considered. Additional application areas are also discussed.

关键词： nonlinear estimation multispectral imagery change detection topographic extraction atmospheric correction

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Nonparametric classification of subpixel materials in multispectral imagery 2

Nonparametric classification of subpixel materials in multis...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Boudreau, E Huguenin, R Karaska, M APPL ANAL INC BILLERICAMA 01821

ISBN: (纸本)0819421391

An effective process for the automatic classification of subpixel materials in multispectral imagery has been developed. The applied analysis spectral analytical process (AASAP) isolates the contribution of specific materials of interest (MOI) within mixed pixels. AASAP consists of a suite of algorithms that perform environmental correction, signature derivation, and subpixel classification. Atmospheric and sun angle correction factors are extracted directly from imagery, allowing signatures produced from a given image to be applied to other images. AASAP signature derivation extracts a component of the pixel spectra that is most common to the training set to produce a signature spectrum and nonparametric feature space. The subpixel classifier applies a background estimation technique to a given pixel under test to produce a residual. A detection occurs when the residual falls within the signature feature space. AASAP was employed to detect stands of Loblolly Pine in a landsat TM scene that contained a variety of species of southern yellow pine. An independent field evaluation indicated that 85% of the detections contained over 20% Loblolly, and that 91% of the known Loblolly stands were detected. For another application, a crop signature derived from a scene in Texas detected occurrences of the same crop in scenes from Kansas and Mexico. AASAP has also been used to locate subpixel occurrences of soil contamination, wetlands species, and lines of communications.

关键词： multispectral subpixel mixed pixel classification signature atmospheric correction spectral analysis MSI remote sensing

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Small-target detection in multispectral imagery with cyclic overlay processing 2

Small-target detection in multispectral imagery with cyclic ...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Schmalz, MS Hu, WC Ritter, GX UNIV FLORIDA DEPT COMP & INFORMAT SCI & ENGNGAINESVILLEFL 32611

ISBN: (纸本)0819421391

The detection of small, hostile targets in multispectral imagery is generally complicated by sensor noise, atmospheric obscurants, and spatial distortions induced by the point-spread function (PSF). Traditional methods for multispectral detection of small targets, such as signature- based discrimination predicated upon deterministic physical models, have not proven robust in the presence of camera noise at low light levels. The additional problem of target variability also confounds a signature-based approach. Due to time-dependent variations in illuminant spectra response, targets can appear to have different spectral properties at different times of day and under various weather conditions. In this paper, we discuss computationally efficient methods for locating targets that differ spectrally from their spatially adjacent backgrounds but are similar to features located elsewhere in the source image. In particular, we note that flicker effects can be produced in which target intensity appears to vary differently with background intensity. Such effects are produced computationally by cyclic overlay processing (COP), which sequentially displays monospectral band images to achieve different perceived flicker rates of target and background. When combined with knowledge about the human visual system (HVS), COP can be successfully used in conjunction with neighborhood operations to segment target regions in highly cluttered imagery. We emphasize the role of target-background contrast in potentiating flicker effects, and discuss algorithms for computing COP. Analyses emphasize computational cost and effectiveness of various COP filter configurations for detecting targets that are similar to, or partially obscured by, surrounding cover or earth. We also discuss the implementation of our algorithms on parallel processors, in particular, the parallel algebraic logic (PAC) architecture currently being implemented in cooperation with Lockheed- Martin and USAF Wright Laboratory

关键词： automated target recognition image and data compression compressive processing

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Atmospheric correction with haze removal including a haze/clear transition region 2

Atmospheric correction with haze removal including a haze/cl...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Richter, R DLR GERMAN AEROSP RES ESTABINST OPTOELECTD-82234 WESSELINGGERMANY

ISBN: (纸本)0819421391

High spatial resolution satellite imagery like Landsat TM and SPOT HRV often contain large regions covered with haze. By applying suitable thresholds clear areas as well as haze and cloud regions can be separated and stored as binary images. Cloud areas are identified in the method presented here, but will not be included in the haze correction, since the ground information cannot be retrieved in optically thick regions. For each spectral band, the haze removal algorithm matches the histogram of the haze areas to the histogram of the clear part of the scene. Due to the binary nature of thresholding this algorithm often causes sharply defined edges at the borders of haze regions. Therefore, a haze boundary region is introduced to generate a smoother transition from haze to clear areas. Two transition methods with a leveled boundary are presented: with fixed weighting and with histogram-dependent weighting. Additionally, the image is atmospherically corrected to remove the remaining atmospheric influence and obtain ground reflectance data.

关键词： atmospheric correction haze removal Landsat TM and SPOT HRV imagery ATCOR2 model

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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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Improved cross-sensor resolution enhancement for landcover products 2

Improved cross-sensor resolution enhancement for landcover p...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Jamison, TA Carroll, EA OBSERVERA INC ALEXANDRIAVA 22306

ISBN: (纸本)0819421391

The overall accuracy of digital landcover products can often be improved through the use of fused imagery products generated by good cross-sensor resolution enhancement algorithms. This paper describes a process for fusing medium resolution multi-spectral data, such as Landsat and SPOT, with National Aerial Photographic Program (NAPP) photographs. The NAPP has a goal of providing coverage of the 48 contiguous United States every 10 years at high spatial resolution [i.e., 2 meter ground resolving distance (GRD)]. NAPP and National High Altitude Photography (NHAP) provide a wealth of current and historic high resolution data for environmental and natural resource studies. Despite their comprehensive coverage and high spatial resolution, these images are often overlooked for use in large-scale computerized classification problems, because: (1) they are photographic 'analog' data stored on film, not digital data on magnetic media; (2) comprehensive support data (e.g., aircraft x, y, z, roll, pitch, and yaw) is lacking; (3) they are not geocoded or orthorectified and random aircraft motion combined with sensor projection make it difficult to georegister; and (4) their radiometric quality varies both within and between images. This paper describes a technique for merging NAPP/NHAP data with lower resolution satellite data such as Landsat and SPOT which results in a fused image product that has the high spatial resolution of the NAPP/NHAP data and the spectral quality of the satellite data. The technique permits the user to utilize this higher resolution data to improve the quality and accuracy of their landcover, change detection, stress analysis, or other remote sensing products. Specific published results show an improvement in the overall accuracy from 79.4% correct classification using Landsat TM (25 meter GSD) alone to over 94.2% correct classification using higher resolution (5 meter GSD) data. We also discuss our future plans related to these techniques and their a

关键词： registration orthorectification resolution enhancement principal components landcover classification NAPP NHAP SPOT multispectral imagery image fusion

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Use of hyperspectral imagery for broad area detection of small targets 2

Use of hyperspectral imagery for broad area detection of sma...

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conference on Imaging Spectrometry ii

作者： Kailey, WF Illing, L Ball Aerospace & Technologies Corp. (United States)

ISBN: (纸本)081942207X

There is a widespread perception that hyperspectral imagery may be useful for military broad area tactical surveillance applications in the not-too-distant future, and the government is planning to demonstrate this technology from space within the next three years. In order to support sensor design for such a demonstration, we have developed an algorithm performance simulation that applies linear unmixing techniques to the problem of quasi-real-time ground clutter suppression in hyperspectral images. Our object is to enable top level sensor design tradeoffs to be evaluated and to derive first order estimates of sensor requirements that could potentially enable fully automated timely detection of time-critical tactical military targets over broad areas against a wide variety of terrain types. We describe a simple algorithm for target detection that is single-pass, completely automated, and requires little or no training to detect targets. Monte Carlo simulations on AVIRIS images have been used to measure the performance of this algorithm under realistic conditions and to derive probability of detection and probability of false alarm versus signal to noise ratio in a pair of test images. Although the parameter space is quite large and the exploratory work is still in progress, early results give valuable insight into design requirements for spaceborne hyperspectral sensors to support broad area tactical surveillance applications.

关键词： Target detection Sensors Signal to noise ratio Detection and tracking algorithms Monte Carlo methods Image filtering hyperspectral target detection Optical filters Short wave infrared radiation hyperspectral imaging

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