Design and Testing of a Java-based Digital SAR Signal Simulation System

Allan, Jeremy M.; Collins, Michael

doi:10.1109/igarss.2006.823

Cited by 4 publications

(10 citation statements)

References 11 publications

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“…The probability distribution after summing is usually difficult to obtain [34], [35]. The authors of [32] used the approximation that the sum of the correlated gamma variables is still gamma distributed and derived the correlation function of the RCS v as [32], [36] ,…”

Section: Statistical Modeling Of a Single Scatterermentioning

confidence: 99%

“…The correlated clutter simulation methods introduced in the two previous sections replicate only the correlated clutter that satisfies the specific statistical distribution of SAR images, without considering the physical process of scattering. The computational SAR (cSAR) simulation system proposed by Allan et al [27], [32], [36] starts from the statistical modeling of the underlying scatterers and simulates correlated K-distributed clutter based on the incoherent scatterer sum model. The incoherent scatterer sum model is degraded from the coherent scatterer model when neglecting the phase [1].…”

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

“…This system begins with the statistical characteristics of a single scatterer, instead of directly modeling the RCS v. It aims to help explore the relationship between SAR image features and the underlying scatterer. Figure 12 illustrates the main simulation steps [27], [32], [36], which include the following: 1) Set randomly positioned scatterers. To produce fully developed speckle, there should be a large number of randomly distributed scatterers in each resolution cell.…”

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

See 2 more Smart Citations

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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Section: Statistical Modeling Of a Single Scatterermentioning

confidence: 99%

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

Section: Simulation Based On the Coherent Scatterer Modelmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Yue

Frery

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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“…1 Different scatterer types within one resolution cell are mutually 2 independent, that is [16], [18]…”

mentioning

confidence: 99%

“…The variance of I is then derived as: Replacing (A.13), (A.14) and (A.15) in (A.12), we obtain: 16) where…”

mentioning

confidence: 99%

A Generalized Gaussian Coherent Scatterer Model for Correlated SAR Texture

Yue

Frery

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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This paper proposes a generalized modeling and 1 simulation approach for correlated SAR texture based on the 2 Gaussian coherent scatterer model. It is rooted in the physics-3 based coherent scatterer assumption where each observation 4 in a SAR image is a coherent sum of multiple underlying 5 Gaussian scatterers. The proposal generalizes existing single-6 point statistical models by allowing the number of scatterers 7 to be a correlated random field. It can also generate the desired 8 spatial correlation texture by stipulating the structure in both 9 the Gaussian scattered field and the number of scatterers. This 10 generalized model is derived theoretically and then validated by 11 both simulations and experiments with SAR data from actual 12 sensors. 13 Index Terms-SAR statistical modeling, Coherent scatterer 14 model, Correlated SAR texture. 15 I. INTRODUCTION 16 S TATISTICAL modeling of SAR images is the theoretical 17 basis of SAR image processing and interpretation: image 18 filtering, image classification, object detection, and other oper-19 ations rely on tractable and expressive models. A good statis-20 tical model should be an effective and efficient representation 21 of the often complicated SAR data. It mainly involves two 22 aspects: the single-point distribution of image pixel intensities, 23 and two-point statistical relations, i.e. the correlation function, 24 which characterizes image texture information. 25 The mainstream models of SAR image statistical modeling 26 are reviewed in [1]. It summarizes more than twenty single-27 point distributions [2]-[13] for different physical scenes, as 28 well as some correlation function models [6], [14]-[18]. It is 29 found that the existing statistical models have three drawbacks, 30 namely, 31 1) many of them are phenomenological methods which do 32 not take into account the actual physical process of 33 scattering; 34 2) each statistical model is usually suitable for only one type 35 of specific scenario of terrain surface e.g. the Rayleigh 36 distribution for homogeneous areas [14], the K distri-37 bution for intermediate inhomogeneous area [4], the G 0 38 Manuscript received XXX; revised XXX.

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SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

Yoo

Kim

2021

IEEE Access

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The effectiveness of using the simulated synthetic aperture radar (SAR) images of military targets in databases for automatic target recognition (ATR) is widely known. However, for simulated target images to be useful, they must be sufficiently similar to measured images; otherwise, they can degrade ATR performance. Two factors affect the quality of simulated SAR images: precision of the associated computer-aided design (CAD) model of the target and the accuracy and speed of the numerical techniques used to solve the electromagnetic problems in SAR image generation. In this study, a method for the 3D CAD modeling of the target is proposed; this method can be used when direct access to the target is not feasible and only indirect information is available. Further, a bistatic image formation concept based on the shooting-and-bouncing-ray technique is adopted; this concept helps achieve an accuracy comparable to that of the monostatic method. Moreover, it helps achieve a highly enhanced computation speed. In combination, these proposals provide an efficient and fast method to generate a database of simulated SAR images that can effectively support ATR activities. We demonstrate the effectiveness of the proposed method by comparing the simulated SAR images with the measured ones using structural similarity as a similarity measure; further, we evaluate the recognition rate obtained with the simulated images. We show that the used similarity measure bears a strong relation with the recognition rate, which is an aspect that may further contribute to considerable time savings when validating and refining simulated image databases.

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Design and Testing of a Java-based Digital SAR Signal Simulation System

Cited by 4 publications

References 11 publications

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

Synthetic Aperture Radar Image Statistical Modeling: Part Two-Spatial Correlation Models and Simulation

A Generalized Gaussian Coherent Scatterer Model for Correlated SAR Texture

SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

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