A parametric model for synthetic aperture radar measurements

Gerry, M.J.; Potter, Lee C.; Gupta, I.J.; Merwe, Andria van der

doi:10.1109/8.785750

Cited by 243 publications

(114 citation statements)

References 20 publications

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“…The term w(m, n) in (1) models the contributions of scatterers other than corners, including the walls, humans, and possible multipath propagation effects. The canonical scattering response S p (m, n,φ p ) of the pth corner (dihedral) reflector with orientation angleφ p is given by [16] …”

Section: Twr Signal Modelmentioning

confidence: 99%

Pattern Matching for Building Feature Extraction

Lagunas

Amin

Ahmad

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to through-the-wall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The feature-based nature of the proposed detector enables corner separation from other indoor scatterers, such as humans.Index Terms-Building dominant scatterers, pattern matching, through-the-wall radar imaging (TWRI).

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Section: Twr Signal Modelmentioning

confidence: 99%

Pattern Matching for Building Feature Extraction

Lagunas

Amin

Ahmad

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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“…We adopt a parametric model describing the sensor data, develop a feature estimation algorithm, and discuss feature uncertainty both for extraction and feature prediction. The model we employ is based on high-frequency approximation of electromagnetic scattering [9], [11] and represents the object of interest as a set of scattering centers. The scattering centers are described by attributes that characterize the scattering center geometry and orientation.…”

Section: A Physical Model For Sensor Data and Feature Extractionmentioning

confidence: 99%

“…In this paper we adopt the physical radar scattering model from Gerry et al [9], which assumes a data collection scenario consistent with SAR imaging. A reference point is defined, and the radar trajectory is required to be coplanar with the reference point.…”

Section: A a Parametric Model For Object Scatteringmentioning

confidence: 99%

Model-based classification of radar images

Chiang

Moses

Potter

2000

IEEE Trans. Inform. Theory

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130

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Abstract-A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating performance sensitivity. The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequencyand aspect-dependent scattering information carried in the image phase.Index Terms-Model-based classification, parametric modeling, point correspondence, radar image analysis.

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“…There are three typical scattering center models: (1) the ideal point scattering center model [7]; (2) the damped exponential model [8]; and (3) the attributed scattering center model [9]. Setting different parameters and resolutions, these models can be converted to each other.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

Xiong

et al. 2018

IJGI

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This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately.

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A parametric model for synthetic aperture radar measurements

Cited by 243 publications

References 20 publications

Pattern Matching for Building Feature Extraction

Pattern Matching for Building Feature Extraction

Model-based classification of radar images

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

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