Entropy-Based Non-Local Means Filter for Single-Look SAR Speckle Reduction

Chan, Débora; Gambini, Juliana; Frery, Alejandro C.

doi:10.3390/rs14030509

Cited by 7 publications

(3 citation statements)

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“…Figure 2 presents the bias and the MSE for the Wieczorkowski and Grzegorzewski [38] criterion, L = 2 case, and for all of the estimators analyzed, except for the Al-Omari (14) and Ebrahimi (15) estimators. These two estimators presented large bias and, thus, were discarded for further analysis.…”

Section: Choice Of the Spacing Parameter M For Non-parametric Estimatorsmentioning

confidence: 99%

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Entropy Estimators in SAR Image Classification

Cassetti

Delgadino

Rey

et al. 2022

Entropy

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Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their own source of illumination. On the one hand, new generations and constellations of Synthetic Aperture Radar (SAR) sensors provide images with high spatial and temporal resolution and excellent coverage. On the other hand, SAR images suffer from speckle noise and need specific models and information extraction techniques. In this sense, the G0 family of distributions is a suitable model for SAR intensity data because it describes well areas with different degrees of texture. Information theory has gained a place in signal and image processing for parameter estimation and feature extraction. Entropy stands out as one of the most expressive features in this realm. We evaluate the performance of several parametric and non-parametric Shannon entropy estimators as input for supervised and unsupervised classification algorithms. We also propose a methodology for fine-tuning non-parametric entropy estimators. Finally, we apply these techniques to actual data.

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Section: Choice Of the Spacing Parameter M For Non-parametric Estimatorsmentioning

confidence: 99%

“…In particular, entropy measures have been widely used for this purpose. Parameter estimation [9], classification [10], procedures for constructing confidence interval and contrast measures [11,12], edge detection [13], and noise reduction filters [14] are among their applications.…”

Section: Introductionmentioning

confidence: 99%

Entropy Estimators in SAR Image Classification

Cassetti

Delgadino

Rey

et al. 2022

Entropy

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“…Filtering a single-look speckled data set using the Shannon and Rényi entropies under G0 model was presented in Ref. 36.…”

Section: Literature Surveymentioning

confidence: 99%

Review on nontraditional perspectives of synthetic aperture radar image despeckling

2022

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Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.

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