DSpace Repository :: Browsing by Author "Rey, Andrea"

Browsing by Author "Rey, Andrea"

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Clasificación de Imágenes SAR utilizando descriptores de textura
(2021-10) Gambini, Juliana; Rey, Andrea; Delrieux, Claudio
"Las imágenes SAR (Sythetic Aperture Radar) y PolSAR (Polarimetric Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al méetodo de captura llamado ruido speckle que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de imagen en presencia de ruido speckle y permitan su interpretación automática. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada pixel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales resultan altamente prometedores para aplicarse en imágenes reales."
Low-cost robust estimation for the single-look 𝒢I0 model using the Pareto distribution
(2020) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C.
"The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ , a scale parameter, and L, the number of looks. Quality estimation of α is essential due to its immediate interpretability. In this letter, we exploit the connection between the G0 I and Pareto distributions. With this, we obtain six estimators that have not been previously used in the SAR literature. We compare their behavior with others in the noisiest case for monopolarized intensity data, namely single look case. We evaluate them using Monte Carlo methods for noncontaminated and contaminated data, considering convergence rate, bias, mean squared error, and computational time. We conclude that two of these estimators based on the Pareto law are the safest choices when dealing with actual data and small samples, as is the case of despeckling techniques and segmentation, to name just two applications. We verify the results with an actual SAR image."
Methods and frameworks for sampling 𝒢I0 data
(2017) Chan, Debora; Rey, Andrea; Gambini, Juliana; Cassetti, Julia; Frery, Alejandro C.
"The 𝒢I0 distribution is a competitive tool for SAR image description. This distribution is useful for describing speckled imagery because it models adequately areas with different degrees of texture. Data simulation is crucial for the development of new methods of automatic interpretation of this type of images. We compare four alternatives for generating data under the 𝒢I0 distribution. The experiments are performed on a variety of programming languages and, a number of criteria to test the fidelity of the generated data are applied."
Sampling from the 𝒢I0 distribution
(2018-12) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C.
"Synthetic Aperture Radar (SAR) images are widely used in several environmental applications because they provide information which cannot be obtained with other sensors. The 𝒢I0 distribution is an important model for these images because of its flexibility (it provides a suitable way for modeling areas with different degrees of texture, reflectivity and signal-to-noise ratio) and tractability (it is closely related to the Snedekor-F, Pareto Type II, and Gamma distributions). Simulated data are important for devising tools for SAR image processing, analysis and interpretation, among other applications. We compare four ways for sampling data that follow the 𝒢I0 distribution, using several criteria for assessing the quality of the generated data and the consumed processing time. The experiments are performed running codes in four different programming languages. The experimental results indicate that although there is no overall best method in all the considered programming languages, it is possible to make specific recommendations for each one."
Statistical properties of the entropy from ordinal patterns
(2022) Chagas, Eduarda T. C.; Frery, Alejandro C.; Gambini, Juliana; Lucini, María M.; Ramos, Heitor S.; Rey, Andrea
"The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair entropy-statistical complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon’s entropy for any model under which the true normalized entropy is neither zero nor one. We obtain the asymptotic distribution from the central limit theorem (assuming large time series), the multivariate delta method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon’s entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon’s entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results."
Texture descriptors for robust SAR image segmentation
(2022-12-28) Rey, Andrea; Gambini, Juliana; Delrieux, Claudio
"SAR (synthetic aperture radar) and PolSAR (polarimetric synthetic aperture radar) images fulfill a fundamental role in Earth observation, due to their advantages over optical images. However, the presence of speckle noise hinders their automatic interpretation and unsupervised use, rendering traditional segmentation tools ineffective. For this reason, advanced image segmentation models are sought to overcome the limitations that make an adequate treat ment of speckled images difficult. We propose a procedure for SAR and PolSAR image clas sification, based on texture descriptors, that combines fractal dimension, a specific probability distribution function, Tsallis entropy, and the entropic index. A vector of local texture features is built using a set of reference regions, then a support vector machine classifier is applied. The proposed algorithm is tested with synthetic and actual monopolarimetric and polarimetric SAR imagery, exhibiting visually remarkable and robust results in coincidence with quantitative qual ity metrics as accuracy and F1-score."

Browsing by Author "Rey, Andrea"

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