Local Subpixel Coregistration of Interferometric Synthetic Aperture Radar Images Based on Fractal Models

Danudirdjo, Donny; Hirose, Akira

doi:10.1109/tgrs.2012.2228488

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…Значимые результаты получены при комплексном использовании этих методов, например, в поляриметрической томографии (Aghababaee, Sahebi, 2018) и поляриметрическом текстурном анализе (Weissgerber et al, 2016). Анализ текстуры путем фрактального анализа используется в интерферометрии для оценки сдвигов и последующего совмещения интерферометрических изображений (Danudirdjo, Hirose, 2013. По аналогии с указанными выше методами комплексирования можно предположить, что поляриметрический анализ текстуры радиолокационных изображений позволит получить дополнительную информацию об объектах исследования, в том числе лесной растительности.…”

Section: Introductionunclassified

Spatial anisotropy assessment of the forest vegetation heterogeneity at various azimuth angles of the radar polarimetric sensing

Бондур

Bondur²,

Чимитдоржиев

et al. 2019

Исследования Земли из Космоса

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The results of studies to assess the texture of L- and C-band radar polarimetric images obtained from SIR-C and ALOS PALSAR-1 satellites for the analysis of forest vegetation characteristics using different signatures are summarized. A fractal polarization signature is proposed for the study, which allows to estimate the spatial anisotropy of forest vegetation inhomogeneities at different azimuthal angles of radar sensing. In addition, the signature of lacunarity is suggested as a tool for qualitative evaluation of the angular distribution of tree branches. The heterogeneities of forest vegetation at the test site near the Baikal Lake have been estimated based on the results of the analysis of fractal dimension and lacunarity at different states of the polarization ellipse.

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Section: Introductionunclassified

Spatial anisotropy assessment of the forest vegetation heterogeneity at various azimuth angles of the radar polarimetric sensing

Бондур

Bondur²,

Чимитдоржиев

et al. 2019

Исследования Земли из Космоса

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“…Therefore, while works [14][15][16][17] were based on the introduction of convenient fractal-based parameters to perform image processing tasks, without entering the problem of associating to them a clear physical meaning, the estimation framework of Di Martino et al [21,22] provides estimates of the fractal dimension of the observed surface, i.e., a parameter with a clear physical meaning that can be easily managed by physicists and geologists for the characterization of natural phenomena. Moreover, works [21,22] forerun (and partly stimulated) the development of SAR processing techniques based on the use of fractal dimension: indeed, recently fractal dimension has been fruitfully used in SAR interferometry, to support coregistration [23], regularization [24], and phase unwrapping [25], and in SAR speckle filtering [26,27]. All these techniques benefit from the availability of accurate estimates of the surface fractal dimension.…”

Section: Introductionmentioning

confidence: 99%

The Role of Resolution in the Estimation of Fractal Dimension Maps From SAR Data

et al. 2017

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This work is aimed at investigating the role of resolution in fractal dimension map estimation, analyzing the role of the different surface spatial scales involved in the considered estimation process. The study is performed using a data set of actual Cosmo/SkyMed Synthetic Aperture Radar (SAR) images relevant to two different areas, the region of Bidi in Burkina Faso and the city of Naples in Italy, acquired in stripmap and enhanced spotlight modes. The behavior of fractal dimension maps in the presence of areas with distinctive characteristics from the viewpoint of land-cover and surface features is discussed. Significant differences among the estimated maps are obtained in the presence of fine textural details, which significantly affect the fractal dimension estimation for the higher resolution spotlight images. The obtained results show that if we are interested in obtaining a reliable estimate of the fractal dimension of the observed natural scene, stripmap images should be chosen in view of both economic and computational considerations. In turn, the combination of fractal dimension maps obtained from stripmap and spotlight images can be used to identify areas on the scene presenting non-fractal behavior (e.g., urban areas). Along this guideline, a simple example of stripmap-spotlight data fusion is also presented.

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“…FBm [14] is a class of random Gaussian process that models self-similarity, 1/f characteristics, and long-range dependence. Numerous studies have suggested fBm as a reliable fractal model for natural surfaces with a minimum number of parameters [15], [16]. FBm models are also studied in relation to the investigation of microwave scattering from natural landscapes [17]- [19], whereas a comprehensive review on fractal surface models and their electromagnetic scattering can be found in [20].…”

Section: Introductionmentioning

confidence: 99%

Anisotropic Phase Unwrapping for Synthetic Aperture Radar Interferometry

Danudirdjo

Hirose

2015

IEEE Trans. Geosci. Remote Sensing

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This paper presents a new phase unwrapping method for synthetic aperture radar interferometry (InSAR). Compared with other phase observations, InSAR data are unique due to the foreshortening effect in SAR images. This effect makes the interferogram phase anisotropic, i.e., the statistics of phase gradients along the ground-range axis are different from those in the azimuth direction. Furthermore, the distribution of phase gradients in the ground-range direction is not symmetric. The proposed method targets the most likely unwrapped phase by considering the foreshortening effect in SAR observation, noise characteristics in InSAR phase data, and fractional Brownian surface as a suitable model for natural topography. Experiments with simulated terrains and real InSAR data show that the method gives comparable or better digital elevation model results than the other tested methods.Index Terms-Digital elevation model (DEM), fractals, phase unwrapping, synthetic aperture radar (SAR). 0196-2892

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Local Subpixel Coregistration of Interferometric Synthetic Aperture Radar Images Based on Fractal Models

Cited by 11 publications

References 31 publications

Spatial anisotropy assessment of the forest vegetation heterogeneity at various azimuth angles of the radar polarimetric sensing

Spatial anisotropy assessment of the forest vegetation heterogeneity at various azimuth angles of the radar polarimetric sensing

The Role of Resolution in the Estimation of Fractal Dimension Maps From SAR Data

Anisotropic Phase Unwrapping for Synthetic Aperture Radar Interferometry

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