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检索条件"任意字段=SPIE Conference on Algorithm for Synthetic Aperture Radar Imagery V"

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Application of point enhancement technique for ship target recognition by HRR

Application of point enhancement technique for ship target r...

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conference on algorithms for synthetic aperture radar imagery X

作者： Cotuk, N Ture, S Cetin, M Arastirma Merkezi Komutanligi TR-81504 Istanbul Turkey

ISBN: (纸本)0819449555

We present an evaluation of the impact of a recently developed point-enhanced high range-resolution (HRR) radar profile reconstruction method on automatic target recognition (ATR) performance. We use several pattern recognition techniques to compare the performance of point-enhanced HRR profiles with conventional Fourier transform-based profiles. We use measured radar data of civilian ships and produce range profiles from such data. We use two types of classifiers to quantify recognition performance. The first type of classifier is based on the nearest neighbor technique. We demonstrate the performance of this classifier using a variety of extracted features, and a number of different distance metrics. The second classifier we use for target recognition involves position specific matrices, which have previously been used in gene sequencing. We compare the classification performance of point-enhanced HRR profiles with conventional profiles, and observe that point enhancement results in higher recognition rates in general.

关键词： high range-resolution radar automatic target recognition nearest neighbor position specific matrices Euclidean metric Manhattan metric Minkowski metric ship target recognition range profile point enhancement signal reconstruction

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Difficulties in the interpretation of synthetic aperture images of targets

Difficulties in the interpretation of synthetic aperture ima...

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conference on algorithms for synthetic aperture radar imagery X

作者： Jonsson, R Stanek, T Dicander, F Ericsson Microwave Syst AB S-43184 Molndal Sweden

ISBN: (纸本)0819449555

In this paper we raise some questions about the nature and consequences of the signal model underlying SAR/ISAR imaging. Here one describe a target with an object function defined over space. The measurements from a sensor is described by some other function, that are related to the object function by an operator that describe the sensor. Imaging is then the inverse problem of finding an approximation to the object function, i.e. the image, given the incomplete measurements. The usual SAR/ISAR object function is a continuous distribution of isotropic point scatterers. This distribution need to be a generalized function in order to describe the observed scattering in some cases, not only for hypothetical point scatterers, but also for a simple object such as a plate. A generalized function is of course not a true function, and there is a conceptual difficulty in viewing an image as an approximation of such an object function. A common practice is to produce calibrated images in the sense that the radar cross section of an isotropic point scatter can be directly read from the level in a magnitude-squared image. We compute such calibrated images for some simple objects such as spheres, plates and dihedrals, and show that they produce levels that not easily can be interpreted. Instead, a non trivial mix of object characteristics and imaging system characteristics such as bandwidth and aperture length influence the level at a certain image point. Even the over all appearance of the image can change. More sophisticated, "super resolving", signal processing methods postulates statistical models for the targets, and we briefly review the assumptions behind some of these methods. All such methods rely on the modeling of prior information about the observed objects. This is not easy to achieve using images, which have quite a varying form even for simple objects. As an alternative, if we are prepared to leave the imaging paradigm, scattering center models give a possibility t

关键词： radar imaging SAR ISAR radar cross section super resolution scattering center model

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The vHF/UHF-band LORA SAR and GMTI system

The VHF/UHF-band LORA SAR and GMTI system

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conference on algorithms for synthetic aperture radar imagery X

作者： Ulander, L Blom, M Flood, B Follo, P Frölind, PO Gustavsson, A Jonsson, T Larsson, B Murdin, D Pettersson, M Rääf, U Stenström, G Swedish Def Res Agcy FOI SE-58111 Linkoping Sweden

ISBN: (纸本)0819449555

The paper describes design principles and presents first results for the airborne LORA (low-frequency radar) system. It covers operating frequencies in the vHF and UHF bands and has both synthetic-aperture radar and ground moving target indication modes. The main motivation for the system is to facilitate detection of man-made targets in a wide range of conditions, i.e. stationary or moving targets as well as targets in open terrain or in concealment under foliage or camouflage. The LORA system will operate in several configurations extending from 20 MHz to 800 MHz. Initial flight trials in 2002 were successfully conducted using the 200400 MHz band. SAR images have been formed from the acquired data and are presented. A second band, 400-800 MHz, has also been completed but has not yet been tested inflight. A third band, 0-90 MHz, is presently being added and will be completed during 2003. The paper also includes results from a recent experiment in northern Sweden which included an extensive target deployment to cover a broad range of operating conditions. vHF-band SAR (20-90 MHz) is compared with high-resolution K-u-band SAR. Results show the superior area-coverage rate of using vHF- compared to K-u-band for robust detection of stationary targets. The high-resolution images provided by the K-u-band SAR are, however, superior for classification and recognition purposes.

关键词： low-frequency radar SAR GMTI vHF-band UHF-band K-u-band foliage penetntion ground targets

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Comparison of chirp scaling and wavenumber domain algorithms for airborne low frequency SAR data processing

Comparison of chirp scaling and wavenumber domain algorithms...

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conference on SAR Image Analysis, Modeling and Techniques v

作者： Potsis, A Reigber, A Alivizatos, E Moreira, A Uzunoglu, N Natl Tech Univ Athens Dept Elect & Comp Engn GR-15780 Athens Greece

ISBN: (纸本)0819446653

In recent years a new class of synthetic aperture radar (SAR) systems, using low frequencies, have emerged. The combination of low frequencies with high bandwidths allows a variety of new applications. Several new fields arise in forestry, biomass estimation and in archaeological and geological exploration. The P-band SAR technology benefits from technological advances in antenna design, low noise amplifiers, band pass filters, digital receiver technology, as well as new processing algorithms [1], [2]. For all the new applications of an airborne P-band SAR system, the high-resolution imaging is an important parameter, but it cannot be easily achieved with conventional processing techniques. In this paper, the performance and limitations of the Extended Chirp Scaling (ECS) algorithm and wavenumber domain Omega-K processing algorithm are analysed and discussed. Additionally, modifications of both algorithms are proposed, which optimise the respective algorithm for processing low frequency, wide-beam and wide-band SAR data. Despite of the inherent limitations of the above mentioned processing algorithms, a deterministic phase error, called "digital phase error", due to digital signal processing characteristics is formulated and its effect to the processed SAR data is analytically described. The analysis is carried out, using simulated low frequency airborne SAR data.

关键词： synthetic aperture radar -SAR p-band SAR omega-K processor extended chirp scaling processing algorithm

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Hyperspectral and SAR imagery data fusion with positive Boolean function

Hyperspectral and SAR imagery data fusion with positive Bool...

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conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery IX

作者： Chang, YL Chen, CT Han, CC Fan, KC Chen, KS Chang, JH St Johns & St Marys Inst Tech Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)0819449539

High-dimensional spectral imageries obtained from multispectral, hyperspectral or even ultraspectral bands generally provide complementary characteristics and analyzable information. Synthesis of these data sets into a composite image containing such complementary attributes in accurate registration and congruence would provide truly connected information about land covers for the remote sensing community. In this paper, a novel feature selection algorithm applied to the greedy modular eigenspaces (GME) is proposed to explore a multi-class classification technique using data fused from data gathered by the MODIS/ASTER airborne simulator (MASTER) and the Airborne synthetic aperture radar (AIRSAR) during the Pacrim II campaign. The proposed approach, based on a synergistic use of these fused data, represents an effective and flexible utility for land cover classifications in earth remote sensing. An optimal positive Boolean function (PBF) based multi-classifier is built by using the labeled samples of these data as the classifier parameters in a supervised training stage. It utilizes the positive and negative sample learning ability of minimum classification error criteria to improve the classification accuracy. It is proved that the proposed method improves the precision of image classification significantly.

关键词： hyperspectral SAR data fusion feature selection positive Boolean function greedy modular eigenspaces minimum classification error

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Fuzzy nearest-mean reclustering of SAR pixel features: assessments on land use classification

Fuzzy nearest-mean reclustering of SAR pixel features: asses...

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conference on SAR Image Analysis, Modeling and Techniques v

作者： Aiazzi, B Alparone, L Baronti, S Bianchini, M Macelloni, G Paloscia, S CNR IFAC Inst Appl Phys Nello Carrara I-50127 Florence Italy

ISBN: (纸本)0819446653

This paper describes a non-parametric algorithm based on fuzzy-reasoning concepts and suitable for land use classification, either supervised or unsupervised, starting from pixel features derived from SAR observations. Pixel vectors composed by simple features calculated from the backscattering coefficient(s) in one or more bands and/or polarizations are iteratively clustered. At each iteration-step, pixels in the scene are classified based on the minimum attained by a weighted Euclidean distance from the centroid representative of each cluster. Upgrade of centroids is iteratively obtained both from the previously obtained classification map and by thresholding a membership function of pixel vectors to each cluster.. Such a function is inversely related to the weighted Euclidean distances from the centroid representative of each cluster. To yield the weighted distances from a pixel vector, its features are weighted by means of progressively refined coefficients, whose calculation still relies on the membership function through a least squares algorithm. Possible "a priori" knowledge coming from ground truth data may be used to initialize the procedure, but is not required. Experiments on MAC-91 NASA/JPL AIRSAR data on the Montespertoli test site show that a total of nine features derived from C-Hv, L-Hv, and P-Hv observations are capable to discriminate seven agricultural cover classes with an overall pixel accuracy of 70%, with one tenth of the ground truth data used for training and the remaining nine tenths for testing the classification accuracy.

关键词： classification clustering fuzzy logic land cover membership function polarimetric SAR relaxation labeling remote sensing supervised and unsupervised learning synthetic aperture radar (SAR) images

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Complex synthetic aperture radar data compression

Complex Synthetic Aperture Radar data compression

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conference on algorithms for synthetic aperture radar imagery IX

作者： Cirillo, FR Poehler, PL Schwartz, DS Rais, H SAIC Melbourne FL 32935 USA

ISBN: (纸本)0819444774

Existing compression algorithms, primarily designed for visible electro-optical (EO) imagery, do not work well for synthetic aperture radar (SAR) data. The best compression ratios achieved to date are less than 10:1 with minimal degradation to the phase data. Previously, phase data has been discarded with only magnitude data saved for analysis. Now that the importance of phase has been recognized for Interferometric synthetic aperture radar (IFSAR), Coherent Change Detection (CCD), and polarimetry, requirements exist to preserve, transmit, and archive the both components. Bandwidth and storage limitations on existing and future platforms make compression of this data a top priority. This paper presents results obtained using a new compression algorithm designed specifically to compress SAR imagery, while preserving both magnitude and phase information at compression ratios of 20:1 and better.

关键词： synthetic aperture radar complex data compression

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Comparison of three different detectors applied to synthetic aperture radar data

Comparison of three different detectors applied to synthetic...

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conference on algorithms for synthetic aperture radar imagery IX

作者： Ranney, K Khatri, H Nguyen, L USA Res Lab AMSRL SERU Adelphi MD 20783 USA

ISBN: (纸本)0819444774

The U.S. Army Research Laboratory has investigated the relative performance of three different target detection paradigms applied to foliage penetration (FOPEN) synthetic aperture radar (SAR) data. The three detectors-a quadratic polynomial discriminator (QPD), Bayesian neural network (BNN) and a support vector machine (SvM)-utilize a common collection of statistics ("feature values") calculated from the fully polarimetric FOPEN data. We describe the parametric variations required as part of the algorithm optimizations, and we present the relative performance of the detectors in terms of probability of false alarm (Pfa) and probability of detection (Pd).

关键词： quadratic polynomial discriminator support vector machine Bayesian neural network

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Ultra-wideband radar data models and target detection with adaptive rank-order filters

Ultra-wideband radar data models and target detection with a...

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conference on algorithms for synthetic aperture radar imagery IX

作者： Mitra, AK Lewis, TL Paul, AS Shaw, AK USAF Res Lab SNRR Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819444774

A number of aspects of ultra-wideband radar target detection analysis and algorithm development are addressed. The first portion of the paper describes a bi-modal technique for modeling ultra-wideband radar clutter. This technique was developed based on an analysis of ultra-wideband radar phenomenology. synthetic image samples that were generated by this modeling process are presented. This sample set is characterized by a number of physical parameters. The second portion of this paper describes an approach to developing a class of filters, known as rank-order filters, for ultra-wideband radar target detection applications. The development of a new rank-order filter denoted as a "discontinuity filter" is presented. Comparative target detection results are presented as a function of data model parameters. The comparative results include "discontinuity filter" performance versus the performance of median filtering and CFAR filtering.

关键词： ultra-wideband radar synthetic aperture radar target detection signal models rank-order filters

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Deconvolution techniques for passive radar imaging

Deconvolution techniques for passive radar imaging

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conference on algorithms for synthetic aperture radar imagery IX

作者： Lanterman, AD Munson, DC Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819444774

Forming images of aircraft using passive radar systems that exploit "illuminators of opportunity," such as commercial television and FM radio systems, involves reconstructing an image from sparse samples of its Fourier transform. For a given flight path, a single receiver-transmitter pair produces one arc of data in Fourier space. Since the resulting Fourier sampling patterns bear a superficial resemblance to those found in radio astronomy, we consider using deconvolution techniques borrowed from radio astronomy, namely the CLEAN algorithm, to form images from passive radar data. Some deconvolution techniques, such as the CLEAN algorithm, work best on images which are well-modeled as a set of distinct point scatterers. Hence, such algorithms are well-suited to high-frequency imaging of man-made targets, as the current on the scatterer surface tends to collect at particular points. When using low frequencies of interest in passive radar, the images are more distributed. In addition, the complex-valued nature of radar imaging presents a complication not present in radio astronomy, where the underlying images are real valued. These effects conspire to present a great challenge to the CLEAN algorithm, indicating the need to explore more sophisticated techniques.

关键词： passive radar radar imaging CLEAN algorithm missing data

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