SAR Imaging: An Autofocusing Method for Improving Image Quality and MFS Image Classification Technique

Malamou, Anna; Pandis, C.; Karakasiliotis, A.; Stefaneas, Petros; Kallitsis, Evangelos; Frangos, Panayiotis

doi:10.1007/978-3-319-04720-1_13

Cited by 3 publications

(2 citation statements)

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“…An object’s movement of higher order as different kinds of accelerations and range displacements causes the ISAR image to be blurred, which requires focusing the object. An autofocusing method for improving synthetic aperture radar image quality and modified fractal signature image classification technique are described in [21]. An algorithm based on keystone transform and time-domain chirp scaling to deal with the space-variant range cell migration in ISAR imaging with ultrahigh range resolution is proposed in [22].…”

Section: Synthetic and Inverse Synthetic Aperture Radar Issuesmentioning

confidence: 99%

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Lazarov

2019

Sensors

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The present work addresses pulsar Crab Nebula emissions from point of view of their modeling and applications for asteroid detection and imaging by applying inverse synthetic aperture radar (ISAR) principles. A huge value of the plasma’s effective temperature is a reason for pulsar emission coherency, a property of great practical meaning for a space objects navigation, localization and imaging. Based on measurement data obtained by Goldstone-Apple Valley and Arecibo radio telescopes, an original time frequency grid mathematical model of pulsar emissions is created. Passive ISAR scenario, a space object’s geometry and a model of pulsar signals reflected from the space object’s surface are also described and graphically illustrated. A new range compression approach for ISAR imaging is suggested and demonstrated. In order to reduce the level of additive white Gaussian noise in signals and enlarge the signal to noise ratio in the final image, coherent summation of multiple complex images is applied. To prove the correctness of the geometry, signal models and theoretical analysis, results of numerical experiments are provided.

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Section: Synthetic and Inverse Synthetic Aperture Radar Issuesmentioning

confidence: 99%

Pulsar Emissions, Signal Modeling and Passive ISAR Imaging

Lazarov

2019

Sensors

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“…In this paper the Modified Fractal Signature (MFS) method is applied to real spaceborne SAR images, provided to us by an International Working Group on SAR techniques (SET 163 Working Group). The main idea concerning this technique is the fact that different terrain types encountered in SAR images yield different characteristic values of 'Fractal Area' curves Aδ and 'Fractal Dimension' (or 'Fractal Signature') curves FD in particular, through which classification of different types of terrain is possible [3]- [5], [8].…”

Section: Introductionmentioning

confidence: 99%

Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images

Malamou

Pandis

Frangos

et al. 2014

ElAEE

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In this paper the Modified Fractal Signature (MFS) method is applied to real Synthetic Aperture Radar (SAR) images provided to our research group by SET 163 Working Group on SAR radar techniques. This method uses the 'blanket' technique to provide useful information for SAR image classification. It is based on the calculation of the volume of a 'blanket', corresponding to the image to be classified, and then on the calculation of the corresponding Fractal Area curve and Fractal Dimension curve of the image. The main idea concerning this proposed technique is the fact that different terrain types encountered in SAR images yield different values of Fractal Area curves and Fractal Dimension curves, upon which classification of different types of terrain is possible. As a result, a classification technique for five different terrain types (urban, suburban, rural, mountain and sea) is presented in this paper.

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