A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Blind Speckle Decorrelation for SAR Image Despeckling

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserve

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR dat

A markovian approach to unsupervised change detection with multiresolution and multimodality SAR data

In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithmfor the challenging case of multimodal SAR data collected by sensors operating atmultiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram-Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts

Deep Learning Methods for Synthetic Aperture Radar Image Despeckling: An Overview of Trends and Perspectives

Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling

Image Segmentation in a Remote Sensing Perspective

Image segmentation is generally defined as the process of partitioning an image into suitable groups of pixels such that each region is homogeneous but the union of two adjacent regions is not, according to a homogeneity criterion that is application specific. In most automatic image processing tasks, efficient image segmentation is one of the most critical steps and, in general, no unique solution can be provided for all possible applications. My thesis is mainly focused on Remote Sensing (RS) images, a domain in which a growing attention has been devoted to image segmentation in the last decades, as a fundamental step for various application such as land cover/land use classification and change detection. In particular, several different aspects have been addressed, which span from the design of novel low-level image segmentation techniques to the de?nition of new application scenarios leveraging Object-based Image Analysis (OBIA). More specifically, this summary will cover the three main activities carried out during my PhD: first, the development of two segmentation techniques for object layer extraction from multi/hyper-spectral and multi-resolution images is presented, based on respectively morphological image analysis and graph clustering. Finally, a new paradigm for the interactive segmentation of Synthetic Aperture Radar (SAR) multi-temporal series is introduced

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks