Esquemas de diferencias finitas en el procesamiento de imágenes

Digital Image processing has been a research area of interest in the last decades, standing out for its applications in the analysis of diagnostic images and astronomical images. In this paper, we perform an overview of edge detection methods through finite-difference to present edge detection as a problem-based learning strategy for numerical differentiation, in order to improve the students’ skills in modeling and algorithmic thinking in numerical analysis courses. In addition, we present image restoration through finite-difference as a problem involving partial differential equations and software tools.El procesamiento de imágenes digitales ha sido un área de investigación de interés en las últimas décadas, destacándose por sus aplicaciones en el análisis de imágenes diagnósticas e imágenes astronómicas. En este artículo, realizamos una descripción general de los métodos de detección de bordes a través de diferencias finitas, con el fin de presentar la detección de bordes como una estrategia de enseñanza de los esquemas de diferencias finitas mediante aprendizaje basado en problemas, buscando desarrollar competencias de modelamiento matemático y pensamiento algorítmico en estudiantes de análisis numérico. Además, presentamos la restauración de imágenes mediante diferencias finitas como un problema que involucra ecuaciones diferenciales parciales y herramientas de software

Riego inteligente: proceso de captura de datos basado en gestión del conocimiento

This paper presents the process of acquiring environmental data that feed an intelligent irrigation control system, which based on the calculation of the evapotranspiration of a crop manages to calculate the water needs of the crop to supply them. It presents the problem of irrigation because a solution based on the Internet of Things (IoT) is considered satisfactory, specifying the variables involved in the process and the characteristics of the data produced by the sensors. After this, it develops the process of capturing data on an IoT architecture based on knowledge management and with the sensing, communication, and analytical phases, referring to the R software components that have been developed to carry out this process, culminating with the projections of irrigation analytics. As irrigation is the main aspect of crop yield, a need inherent to the field sector that is not yet automated and that seeks solutions to the conditions of the Colombian countryside is supplied.El presente artículo describe el proceso de adquisición de datos medioambientales que alimentan un sistema de control de riego inteligente el cual, basado en el cálculo de la evapotranspiración de un cultivo, logra calcular las necesidades hídricas del mismo para suplirlas. Luego de plantearse la problemática del riego, y la justificación de una solución basada en Internet de las Cosas (IoT) como satisfactoria, se precisan las variables que intervienen en el proceso y las características de los datos que producen los sensores; se desarrolla el proceso de captura de datos sobre una arquitectura IoT basada en gestión del conocimiento con las fases de: sensado, comunicación y analítica, refiriendo los componentes del software R que se han implementado para realizar este proceso, culminando con las proyecciones de analítica del riego. Al ser el riego el aspecto principal del rendimiento de un cultivo se concluye que se suple una necesidad inherente al sector del campo -que aún no está automatizado- proponiéndose una solución para las condiciones específicas del campo colombiano

Displacement Estimation by Maximum Likelihood Texture Tracking

International audienceThis paper presents a novel method to estimate displacement by maximum-likelihood (ML) texture tracking. The observed polarimetric synthetic aperture radar (PolSAR) data-set is composed by two terms: the scalar texture parameter and the speckle component. Based on the Spherically Invariant Random Vectors (SIRV) theory, the ML estimator of the texture is computed. A generalization of the ML texture tracking based on the Fisher probability density function (pdf) modeling is introduced. For random variables with Fisher distributions, the ratio distribution is established. The proposed method is tested with both simulated PolSAR data and spaceborne PolSAR images provided by the TerraSAR-X (TSX) and the RADARSAT-2 (RS-2) sensors

Maximum Likelihood Shift Estimation using High Resolution Polarimetric SAR Clutter Model

International audienceThis paper deals with a Maximum Likelihood (ML) shift estimation method in the context of High Resolution (HR) Polarimetric SAR (PolSAR) clutter. Texture modeling is exposed and the generalized ML texture tracking method is extended to the merging of various sensors. Some results on displacement estimation on the Argentiere glacier in the Mont Blanc massif using dual-pol TerraSAR-X (TSX) and quad-pol RADARSAT-2 (RS2) sensors are finally discussed

Statistical Classification for Heterogeneous Polarimetric SAR Images

International audienceThis paper presents a general approach for high-resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently "close" from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts

H/α Unsupervised Classification for Highly Textured Polinsar Images using Information Geometry of Covariance Matrices

International audienceWe discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the class centers in the polarimetric H/α unsupervised classification process. We show that the class centers remain more stable during the iteration process, leading to a different interpretation of the H/α /A classification. This technique can be applied both on classical Sample Covariance Matrix and on Fixed Point covariance matrices. Used jointly with the Fixed Point covariance matrix estimate, this technique can give more robust results when dealing with high resolution and highly textured polarimetric SAR images classification

Doping-induced metal-insulator transition in aluminum-doped 4H silicon carbide

International audienceWe report an experimental determination of the doping-induced metal-insulator transition in aluminum-doped 4H silicon carbide. Low temperature transport measurements down to 360 mK and temperature dependent Raman experiments down to 5 K, together with secondary ion mass spectroscopy profiling, suggest a critical aluminum concentration lying between 6.4 and 8.7 1020 cm−3 for the metal-insulator transition in these epilayers grown by the vapor-liquid-solid technique. Preliminary indications of a superconducting transition in the metallic sample are presented

On the Extension of the Product Model in Polsar Processing for Unsupervised Classification Using Information Geometry of Covariance Matrices

International audienceWe discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the centers of class in the polarimetric H/α unsupervised classification process. We can show that the centers of class will remain more stable during the iteration process, leading to a different interpretation of the H/α/A classification. This technique can be applied both on classical SCM and on Fixed Point covariance matrices. Used jointly with the Fixed Point CM estimate, this technique can give nice results when dealing with high resolution and highly textured polarimetric SAR images classification

Using Quad‐Pol and Single‐Pol RADARSAT‐2 Data for Monitoring Cold Alpine and Outlet Antarctic Glaciers

International audienceThis paper presents some applications of the Maximum Likelihood (ML) texture tracking on displacement estimation of some alpine and antarctic glaciers surfaces. This method is adapted to the statistical characteristic of the new High Resolution (HR) Polarimetric SAR (Pol- SAR) data. The ML texture tracking method is firstly reminded and a statistical model of HR PolSAR data is explained. The main part of this paper is focused on the application of this method on glaciers monitoring. Three different glaciers have been chosen to test the algorithm: a cold alpine glacier, a temperate alpine glacier and an outlet antarctic glacier. The accuracy and limits of the method are highlighted in each case and results application is discussed