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Unsupervised segmentation of PolSAR data with complex Wishart and Gm0 distributions and Shannon entropy

Nascimento, Abraão D. C. ; Ferreira, Jodavid A. ; Frery, Alejandro C.

Statistics and computing, 2023-12, Vol.33 (6) [Periódico revisado por pares]

New York: Springer US

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Título:
Unsupervised segmentation of PolSAR data with complex Wishart and Gm0 distributions and Shannon entropy
Autor: Nascimento, Abraão D. C. ; Ferreira, Jodavid A. ; Frery, Alejandro C.
Assuntos: Algorithms ; Artificial Intelligence ; Computer Science ; Dielectric properties ; Entropy ; Entropy (Information theory) ; Image acquisition ; Image segmentation ; Mathematical analysis ; Matrices (mathematics) ; Original Paper ; Probability and Statistics in Computer Science ; Random noise ; Remote sensing ; Statistical Theory and Methods ; Statistics and Computing/Statistics Programs ; Synthetic aperture radar ; Weather
É parte de: Statistics and computing, 2023-12, Vol.33 (6)
Descrição: Polarimetric synthetic aperture radar (PolSAR) systems are powerful remote sensing instruments. Despite their ability to acquire images in all weather conditions, day and night, and to detect dielectric properties and structures, PolSAR images are corrupted by multidimensional speckle. Such contamination, resulting from the use of coherent illumination, does not obey the classical hypothesis of additive Gaussian noise and therefore require special treatment. Moreover, the data are structured as positive-definite Hermitian matrices. The Wishart and G m 0 laws, both defined on the set of all positive definite Hermitian matrices, successfully describe fully PolSAR returns. The Shannon entropy is a scalar that measures the contrast between two PolSAR observations in this context. We obtain the Shannon entropy of these models and, using the h - ϕ entropy formalism, its asymptotic distribution. With these two elements, and using the Stochastic Expectation-Maximisation algorithm, we obtain better segmentations than those provided by the k-means and k-medoids with nine-dimensional inputs. We show applications to actual PolSAR data. Our results show that G m 0 entropy-based segmentation is better suited for images where the class features are not well separated, while its Wishart counterpart gives the best results in scenarios with more separable regions.
Editor: New York: Springer US
Idioma: Inglês

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Unsupervised segmentation of PolSAR data with complex Wishart and Gm0 distributions and Shannon entropy

Nascimento, Abraão D. C. ; Ferreira, Jodavid A. ; Frery, Alejandro C.

New York: Springer US

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