An optimal multiedge detector for SAR image segmentation,”

Abstract-Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. We propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented. Index Terms-Edge detection, multiedge model, region merging, segmentation, speckle, synthetic aperture radar (SAR), watershed algorithm

Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection

This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented

Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field

International audienceThis paper introduces a Bayesian non parametric (BNP) model associated with a Markov random field (MRF) for detecting changes between remote sensing images acquired by homogeneous or heterogeneous sensors. The proposed model is built for an analysis window which takes advantage of the spatial information via an MRF. The model does not require any a priori knowledge about the number of objects contained in the window thanks to the BNP framework. The change detection strategy can be divided into two steps. First, the segmentation of the two images is performed using a region based approach. Second, the joint statistical properties of the objects in the two images allows an appropriate manifold to be defined. This manifold describes the relationships between the different sensor responses to the observed scene and can be learnt from a training unchanged area. It allows us to build a similarity measure between the images that can be used in many applications such as change detection or image registration. Simulation results conducted on synthetic and real optical and synthetic aperture radar (SAR) images show the efficiency of the proposed method for change detection

CFAR Edge Detector for Polarimetric SAR Images

Abstract—Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data. Index Terms—Complex Wishart distribution, edge detection, polarimetry, synthetic aperture radar (SAR), Wishart likelihoodrati

Modelo mejorado para la estimación de puntos de borde en imágenes SAR polarimétricas

El Radar de Apertura Sintética polarimétrico es un tipo especial de radar ampliamente utilizado en teledetección, permite obtener imágenes de alta resolución a gran distancia. La interpretación automática de estas imágenes es una tarea difícil, contienen gran volumen de información y se encuentran contaminadas con ruido speckle no Gaussiano ni aditivo. Este ruido hace necesario utilizar métodos estadísticos para el procesamiento y análisis de estas imágenes. El modelo de distribución estadística más utilizado en este tipo de imágenes generadas a partir de datos multilook polarimétricos es la distribución Wishart compleja. El tiempo de procesamiento para estimar la “posición de los puntos de bordes (PPB)” es relevante y existen trabajos de investigación que analizan y comparan los tiempos de procesamiento de diferentes métodos de estimación de PPB. Este trabajo tiene por objetivo reducir el tiempo de procesamiento en la estimación de PPB. A tal efecto, se analizan técnicas de estimación de PPB que usan distribución Wishart compleja, máxima verosimilitud y distancias estocásticas. Aplicando el modelo de señal en tiempo discreto se obtienen se obtienen expresiones analíticas optimizadas para la estimación de la posición de los puntos de borde y se evalúan los tiempos de procesamiento mediante Montecarlo en imágenes simuladas.Red de Universidades con Carreras en Informátic

리모트센싱을 이용한 필지별 토지이용현황 조사방법 연구(Land use/cover classification method for individual land parcel in high spatial resolution remotely sensed imagery)

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