Examinando por Materia "RADAR DE APERTURA SINTETICA"

Mostrando1 - 10 de 10
  • Artículo de Publicación Periódica
    Comparing samples from the 𝒢0 distribution using a geodesic distance
    (2020-06) Frery, Alejandro C.; Gambini, Juliana
    "The 𝒢0 distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the 𝒢0 distribution using a geodesic distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the 𝒢0 distribution. We propose three tests based on the GD which combine the tests presented in Naranjo-Torres et al. (IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):987–997, 2017), and we estimate their probability distributions using permutation methods."
  • Proyecto final de Grado
    Estudio sobre la aplicación del método SIFT en imágenes SAR
    (2017) Agopian, Michel; Colloca, Tomás; Di Nucci, Nicolás Santiago; Gambini, Juliana
    "En este trabajo se estudia la aplicación del método SIFT y su variante: el algoritmo SAR-SIFT desarrollado para aplicarse en imágenes SAR. Se introduce primero el motivo de interés del uso de este tipo de imágenes, mencionando que el ruido speckle que presentan dificulta la aplicación de los métodos de procesamiento de imágenes como el método SIFT."
  • Ponencia en Congreso
    Evaluación del error en la detección de puntos de borde en imágenes SAR polarimétricas
    (2017-04) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C.
    "El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."
  • Artículo de Publicación Periódica
    The geodesic distance between 𝒢I0 models and its application to region discrimination
    (2017-03) Naranjo-Torres, José; Gambini, Juliana; Frery, Alejandro C.
    "The 𝒢I0 distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter, and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between 𝒢I0 models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of identifying edges between regions with different texture, and quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances."
  • Póster
    Imágenes SAR polarimétricas: evaluación del error en la detección de puntos de borde
    (2017) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C.
    "El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."
  • Artículo de Publicación Periódica
    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."
  • Ponencia en Congreso
    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."
  • Artículo de Publicación Periódica
    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."
  • Ponencia en Congreso
    SAR Image segmentation based on multifractal features
    (2019-09) Pacheco, Cristian; Gambini, Juliana; Delrieux, Claudio
    "Synthetic Aperture Radar (SAR) imaging is based on airborne or satellite active microwave sensors that can capture the earth surface by emitting a signal and receiving the backscattered signal that forms the resulting image. Since microwave radiation is not interfered by sunlight and can pass through clouds, SAR imagery can be generated oblivious to weather and daylight conditions. However, the active nature of the imaging process determines that SAR images are contaminated by an inherent speckle noise that may degrade significantly the quality and usefulness of the images, and specific noise-removal processes may also filter out relevant textural information. In this article, we propose a texture-based method that can be applied for region segmentation in SAR imagery. The method is based on local analysis of the multifractal spectrum and a clustering procedure. The outcomes obtained both with synthetic and real SAR images show better region segmentation results than with state-of-the-art proposals."
  • Ponencia en Congreso
    Speckle noise reduction in SAR images using information theory
    (2020) Chan, Debora; Gambini, Juliana; Frery, Alejandro C.
    "In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution. The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred."