# Alejandro C. FreryVictoria University of Wellington · School of Mathematics, Statistics and Operations Research

Alejandro C. Frery

Doutor em Computação Aplicada

## About

418

Publications

60,785

Reads

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5,403

Citations

Citations since 2017

Introduction

Alejandro C. Frery is the founder of LaCCAN - Laboratório de Computação Científica e Análise Numérica, Universidade Federal de Alagoas. Alejandro does research in Statistical Computing, Image Processing, in particular in SAR/PolSAR data analysis. He is currently Professor of Statistics and Data Science with the School of Mathematics and Statistics, Victoria University of Wellington, New Zealand.

Additional affiliations

May 2020 - present

June 2003 - present

**Universidade Federal de Alagoas**

March 1996 - May 2003

## Publications

Publications (418)

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when u...

Several approaches and descriptors have been proposed to characterize the purity of coherency or density matrices describing physical states, including the polarimetric purity of 2D and 3D partially polarized waves. This work introduces two interpretations of the degree of purity: one derived from statistics and another from algebra. In the first o...

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,...

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remote...

Polarimetric synthetic aperture radar (PolSAR) systems are an important remote sensing tool. Such systems can provide high spacial resolution images, but they are contaminated by an interference pattern called multidimensional speckle. This fact requires that PolSAR images receive specialised treatment; particularly, tailored models which are close...

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,...

In SAR Image Analysis — A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practit...

In the context of non-parametric analysis of time series, the use of Ordinal Patterns combined with descriptors of Information Theory proved being powerful in characterizing processes underlying the data dynamics. Two are prominent among those descriptors: Shannon's entropy and Statistical Complexity; together, they define the Entropy-Complexity Pl...

The G0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathscr {G}^0$$\end{document} distribution is an apt model for speckled data, such as SAR imagery, because it pos...

Bernoulli Distribution is the basic discrete distribution. It models the outcome of a dichotomic random experiment. A binomial random variable is the result of counting the successes in n independent identically distributed Bernoulli trials. Two properties make the Beta distribution specially apt for image processing operations: its finite support,...

This chapter derives the basic properties of Synthetic Aperture Radar (SAR) data, starting from the complex scattering vector and then reaching the Exponential and Gamma distributions. With this, it covers what many authors call fully developed speckle , or speckle for textureless targets . A SAR sensor emits electromagnetic pulses and records the...

This chapter deals with two challenging topics: filter assessment and robustness. The key concept regarding the assessment of a despeckling filter relies on the metrics used for the evaluation of its performances. Statistics applied to actual problems cannot be understood without discussing all relevant matters related to robustness, where robustne...

This chapter discusses some fundamental background and tools about the data analysis, as the statistical properties of Synthetic Aperture Radar (SAR) data are extremely important for SAR image processing. It also introduces the spatial property of SAR images and some operations about image processing. It is well known that many useful R functions c...

This chapter focuses on Synthetic Aperture Radar (SAR) data. It first provides an introduction to SAR systems and detail how data are acquired, whereas the main parameters related to SAR systems such as azimuth resolution and range resolution and the main acquisition modes. The chapter introduces radar systems which is included to better deal with...

Despeckling filters based on statistical methods for Synthetic Aperture Radar (SAR) imagery have been chosen to put into practice the statistical models studied due to their relevance in the analysis and interpretation of SAR images. This chapter discusses the filters based on the Mean, the Median, and the Lee filters that stem from this statistica...

This chapter deals with reproducibility and replicability. It is important to notice that reproducibility and replicability should permeate the whole scientific life. Reproducibility is at the very core of experimental sciences. Whatever result has been obtained by a researcher must be obtained easily by another researcher at low cost, without tric...

Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their o...

This article serves two purposes. Firstly, it surveys the Bandt and Pompe methodology for the statistical community, stressing topics that are open for research. Secondly, it contributes towards a better understanding of the statistical properties of that approach for time series analysis. The Bandt and Pompe methodology consists of computing infor...

The degree of polarimetric purity is an invariant dimensionless quantity that characterizes the closeness of a polarization state of a wave to a pure state and is related to the Von Neumann entropy. The polarimetric purity of a plane wave characterized by the second-order statistics (i.e., the covariance matrix) is uniquely described by the degree...

Speckle is an interference phenomenon that contaminates images captured by coherent illumination systems. Due to its multiplicative and non-Gaussian nature, it is challenging to eliminate. The non-local means approach to noise reduction has proven flexible and provided good results. We propose in this work a new non-local means filter for single-lo...

The generalized degree of polarimetric purity is a vital descriptor widely studied and interpreted for electromagnetic wave characterization. It is invariant under the rotation of the reference frame. In this work, we first propose an alternate expression of this purity measure using the mean (m) and standard deviation (s) of the real positive eige...

Using full-polarimetric Synthetic Aperture Radar (SAR) data, the model-free four-component scattering power decomposition technique overcomes several limitations of other model-based target decomposition approaches. In particular, all the power components are roll-invariant, non-negative. Alongside this, an unsupervised clustering technique was pro...

We investigate the use of ordinal pattern transition graphs in the characterization of PolSAR image textures. Chagas et al. [4] proposed WATG, a transition graph analysis which includes the amplitude information of texture samples in the graph edges. This approach led to good results in the characterization and classification of data from urban, oc...

The Earth’s environment is continually
changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant c...

We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two memory layers: one graph layer and one output layer. The memory layer captures SST temporal changes via temporal convolution units and gate linear units. The graph layer encodes SST spatial changes in terms of char...

We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polari...

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial...

This paper introduces a technique for using Recurrent Neural Networks to forecast Ae. aegypti mosquito (Dengue transmission vector) counts at neighbourhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situ data in two Brazilian cities, and compared with state-of-the-art multi-outp...

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model-based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branchin...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Reports on GRSS society Chapter activities in Brazil.

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population....

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branchin...

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branchin...

Following on the first part of our review of synthetic aperture radar (SAR) image statistical modeling [1], which concerns single-pixel statistical models, this article extends our discussion to spatial correlation analysis, focusing on SAR spatial correlation models and SAR clutter simulation methods. Two types of spatial correlation models, the p...

We propose a new technique for SAR image texture characterization based on ordinal pattern transition graphs. The proposal consists in (i) transforming a 2-D patch of data into a time series using a Hilbert Space Filling Curve, (ii) building an Ordinal Pattern Transition Graph with weighted edges; (iii) obtaining a probability distribution function...

In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Rényi 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...

In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version us...

The scattering information from targets is either estimated by fitting suitable scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. In this study, a new roll-invariant scattering-type parameter is introduced, which jointly uses the 3D Barakat degree of polarisation and...

The use of Bandt-Pompe probability distributions and descriptors of Information Theory has been presenting satisfactory results with low computational cost in the time series analysis literature. However, these tools have limitations when applied to data without time dependency. Given this context, we present a newly proposed technique for texture...

Polarimetric synthetic aperture radar (PolSAR) sensors have reached an essential position in remote sensing. The images they provide have speckle noise, making their processing and analysis challenging tasks. We discuss an edge detection method based on the fusion of evidences obtained in the intensity channels hh, hv, and vv of PolSAR multilook im...

This work deals with the journals in the area of Education classified in the last available Brazilian Qualis database, period 2013–2016, seeking to analyze the alignment of the strata to international bibliometric criteria. The impact of a journal implies its internationalization, which is a standard adopted worldwide. This subject has been gaining...

Remote Sensing is both an active research area and the source of valuable information for decision-making. Many actors play a fundamental role in Remote Sensing, from industry (public or private) to large or small research groups. From that intensive activity, methods, algorithms, and techniques are continuously published or broadcasted through pap...

With the rapid development of spaceborne synthetic aperture radar (SAR) technology and the acquisition of a large volume of SAR images, SAR image interpretation has become an urgent and difficult research topic. SAR image statistical modeling is one of the theoretical foundations for SAR image interpretation. It is of great value for the in-depth a...

Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objec...

We review good scientific practices that may lead to more success and less stress of the active researcher. We will emphasize the importance of reproducibility in every step of the research process: from the conception and writing of the proposal to the publication and support of the results.

In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version us...

Crop growth monitoring using compact-pol Synthetic Aperture Radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI). The CpRVI is derived using the concept of a geodesic distance between Kennaugh matrices project...

Polarimetric Synthetic Aperture Radar (PolSAR)has achieved an important position as a remote sensing imagingmethod. However, PolSAR images are contaminated with specklenoise, making its processing and analysis challenging tasks. Thepresent study discusses a detection method based on the fusionof evidences obtained in the intensity channels of multi...

Submitted paper to IEEE-TGRS at January 10th, 2020.

Submitted paper to IEEE-TGRS at January 10th, 2020.

This manuscript was submitted on 31 December 2019 to IEEE Transactions on Geoscience and Remote Sensing.
Abstract: Incoherent target decomposition techniques provide unique scattering information from polarimetric SAR data either by fitting appropriate scattering models or by optimizing the ``received" wave intensity through the diagonalization of...

This manuscript was submitted on 31 December 2019 to IEEE Transactions on Geoscience and Remote Sensing.
Abstract: Incoherent target decomposition techniques provide unique scattering information from polarimetric SAR data either by fitting appropriate scattering models or by optimizing the ``received" wave intensity through the diagonalization of...

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric SAR data. This is achieved either by fitting appropriate
scattering models or by optimizing the received wave intensity
through the diagonalization of the coherency (or covariance)
matrix. As such, the received wave information dep...

This article proposes a generalized modeling and simulation approach for correlated synthetic aperture radar (SAR) texture based on the Gaussian coherent scatterer model. It is rooted in the physics-based coherent scatterer assumption where each observation in an SAR image is a coherent sum of multiple underlying Gaussian scatterers. The proposal g...

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 GI0 distribution is flexible enough to model different degrees of texture in speckled data. It is inde...

Strategies based on the extraction of measures from ordinal patterns transformation, such as probability distributions and transition graphs, have reached relevant advancements in distinguishing different time series dynamics. However, the reliability of such measures depends on the appropriate selection of parameters and the need for large time se...

A brief discussion about Science, the role of publications in Science, and the importance of reproducibility and replicability.

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each net...

Executable of the structure tensor filter (64bit Microsoft Windows executable - tested under Windows 10).
For more information, please check www.ctim.es/demo111/