Improving Log-Cumulant-Based Estimation of Heterogeneity Information in SAR Imagery

Neto, Jeova Farias Sales Rocha; Rodrigues, Francisco Alixandre Ávila

doi:10.1109/lgrs.2023.3305119

Cited by 2 publications

(1 citation statement)

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“…Values near zero greater than −3 suggest extremely textured targets, such as urban zones [39]. As the value decreases, it indicates regions with moderate texture (in the [−6, −3] region), related to forest zones, while values below −6 correspond to textureless regions, such as pasture, agricultural fields, and water bodies [40].…”

Section: Simulated Datamentioning

confidence: 99%

Identifying Heterogeneity in SAR Data with New Test Statistics

Frery,

Alpala,

Nascimento

2024

Remote Sensing

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This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.

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Section: Simulated Datamentioning

confidence: 99%