Entropy Estimators in SAR Image Classification

Cassetti, Julia; Delgadino, Daiana; Rey, Andrea; Frery, Alejandro C.

doi:10.3390/e24040509

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…The first is suitable for fully developed speckle and is a limiting case of the second model. This is interesting due to its versatility in accurately representing regions with different roughness properties [24]. We denote Z ∼ Γ SAR (L, µ) and Z ∼ G 0 I (α, γ, L) to indicate that Z follows the distributions characterized by the respective probability density functions (pdfs):…”

Section: Statistical Modeling Of Intensity Sar Datamentioning

confidence: 99%

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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: Statistical Modeling Of Intensity Sar Datamentioning

confidence: 99%

“…Several authors have explored adaptations to Vasicek's estimator. We consider three estimators known for their superior performance [24]:…”

Section: Estimation Of the Shannon Entropymentioning

confidence: 99%

Identifying Heterogeneity in SAR Data with New Test Statistics

Frery,

Alpala,

Nascimento

2024

Remote Sensing

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“…The entropy is ultimately defined based on this probability distribution. Classical one-dimensional image entropy includes fuzzy entropy [4,5], Kapur entropy [6], cross entropy [7,8], and Shannon entropy [9]. In addition to being applied to image segmentation or classification, image entropy has also been applied to image filtering and denoising [10,11].…”

Section: Image Entropymentioning

confidence: 99%

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Cao

Wang

2023

Atmosphere

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The fog density level, as one of the indicators of weather conditions, will affect the management decisions of transportation management agencies. This paper proposes an image-based method to estimate fog density levels to improve the accuracy and efficiency of analyzing fine meteorological conditions and validating fog density predictions. The method involves two types of image entropy: a two-dimensional directional entropy derived from four-direction Sobel operators, and a combined entropy that integrates the image directional entropy and grayscale entropy. For evaluating the performance of the proposed method, an image test set and an image training set are constructed; and each image is labeled as heavy fog, moderate fog, light fog, or fog-free according to the fog density level of the image based on a user study. Using our method, the average accuracy rates of image fog level estimation were 77.27% and 79.39% on the training set using the five-fold cross-validation and the test set, respectively. Our experimental results demonstrate the effectiveness of the proposed combined entropy for image-based fog density level estimation.

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“…The entropy associated with a random variable is a measure of its uncertainty or diversity, taking large values for a highly unpredictable random variable (i.e., all outcomes equally probable) and low values for a highly predictable one (i.e., one or few outcomes much more probable than the others). As such, the concept has found multiple applications in a variety of fields including but not limited to nonlinear dynamics, statistical physics, information theory, biology, neuroscience, cryptography, and linguistics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy Estimators for Markovian Sequences: A Comparative Analysis

De Gregorio,

Sánchez,

Toral

2024

Entropy

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Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performances vary depending on the system being studied and the available data size. In this work, we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation, and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields.

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Entropy Estimators in SAR Image Classification

Cited by 8 publications

References 48 publications

Identifying Heterogeneity in SAR Data with New Test Statistics

Identifying Heterogeneity in SAR Data with New Test Statistics

Fog Density Evaluation by Combining Image Grayscale Entropy and Directional Entropy

Entropy Estimators for Markovian Sequences: A Comparative Analysis

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