&lt;title&gt;Performance of VHF-band SAR change detection for wide-area surveillance of concealed ground targets&lt;/title&gt;

Ulander, Lars M. H.; Pierson, William E.; Lundberg, Magnus; Follo, Peter; Frölind, P.-O.; Gustavsson, A.

doi:10.1117/12.542070

Cited by 33 publications

(30 citation statements)

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“…11,12 By having a reference image with which to compare a mission image, it is possible to cancel much of the background with automated algorithms. Detection of changes (either pixel or object level) has been found to be much more accurate and more robust to challenging operating conditions than direct detection of targets in single-look sensor data.…”

Section: Change Detectionmentioning

confidence: 99%

Improved automatic target recognition (ATR) value through enhancements and accommodations

Ross

Goodwon

2006

SPIE Proceedings

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There is a strong and growing need for automatic target recognition (ATR) technologies. Those technologies have made great strides; however, there is a general sense that they are not having the full impact desired. This paper develops a value-based framework for considering how ATR technology can be made more relevant and then introduces and expands on two elements within that framework: "enhancements" and "accommodations". Value is used here as the degree to which a technology's benefits exceed the technology's costs. Value may be improved by increasing benefits or decreasing costs; but it may be as important that the uncertainty about benefits and costs be reduced. Enhancements and accommodations are distinguished here from the "core ATR". While it is generally appreciated that improved core ATR performance could improve value, enhancements and accommodations might be overlooked by those focused on ATRs. Enhancements are ways of making the overall system, inclusive of a core ATR, more capable. Accommodations are ways of making the problem easier for the core ATR. An example enhancement is technology to fuse the output of the core ATR with other sources. An example accommodation is for the user to agree to limit the target set to large, and therefore more easily recognized, objects. This paper encourages the consideration of this framework and outlines a number of candidates for enhancements and accommodations for synthetic aperture radar (SAR) ATR, including humans-in-the-loop, change detection, fusion, modeling confusers, group detection, adaptive algorithms, class make-up, and scene-based decisions.

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Section: Change Detectionmentioning

confidence: 99%

Improved automatic target recognition (ATR) value through enhancements and accommodations

Ross

Goodwon

2006

SPIE Proceedings

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“…Common tasks in synthetic aperture radar (SAR) statistical image processing include the identification and classification of distinct ground type [1][2][3][4][5], modeling [6][7][8][9], and change detection [10][11][12][13]. In special, wavelength-resolution low-frequency SAR systems are useful for natural disasters monitoring, foliage-penetrating applications, and detection of concealed targets [14].…”

Section: Introductionmentioning

confidence: 99%

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Palm

Alves²,

Pettersson³

et al. 2020

Sensors

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This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

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“…Recently, target detection performance has been studied in more detail for VHF band SAR [1][2][3]. However, characterization of target detection system performance over a wide range of operating conditions from measured data alone is an expensive process, requiring the collection of measured target and clutter data for a large number of scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…As frequency decreases, the wavelength increases and the radar signal is better able to penetrate the leafy structure of the forest canopy and interrogate the region below. FOPEN SAR systems can thus be used for finding vehicles and other large man-made structures hidden beneath the foliage [1][2][3] as well as for forestry applications that include the measurement of biomass [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Model for Generating Synthetic VHF SAR Forest Clutter Images

Jackson

Moses

2009

IEEE Trans. Aerosp. Electron. Syst.

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We propose a model for generating low-frequency synthetic aperture radar (SAR) clutter that relates model parameters to physical characteristics of the scene. The model includes both distributed scattering and large-amplitude discrete clutter responses. The model also incorporates the SAR imaging process, which introduces correlation among image pixels. The model may be used to generate synthetic clutter for a range of environmental operating conditions for use in target detection performance evaluation of the radar and automatic target detection/recognition algorithms. We derive a statistical representation of the proposed clutter model's pixel amplitudes and compare with measured data from the CARABAS-II SAR. Simulated clutter images capture the structure and amplitude responses seen in the measured data. A statistical analysis shows an order of magnitude improvement in model fit error compared with standard maximum-likelihood (ML) density fitting methods.

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<title>Performance of VHF-band SAR change detection for wide-area surveillance of concealed ground targets</title>

Cited by 33 publications

References 0 publications

Improved automatic target recognition (ATR) value through enhancements and accommodations

Improved automatic target recognition (ATR) value through enhancements and accommodations

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

A Model for Generating Synthetic VHF SAR Forest Clutter Images

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