Synthetic aperture radar (SAR) has been successfully used as a remote sensing tool. However, SAR images are contaminated by speckle noise and require specialized post processing procedures; e.g, tailored segmenters. The G $^0$ $_1$ distribution is a flexible model for SAR intensities because its ability at describing heterogeneous clutters. Further, applying information theory measures (e.g., entropy) to extract features in SAR imagery processing has achieved a prominent position. In this paper, we derive both a closed-form expression for the G $^0$ $_1$ Shannon entropy and some of its mathematical properties. Consequently new entropy-based segmentation procedures for multidimensional SAR intensities—assuming independence or some dependence pattern—are also proposed. Finally, applications to real SAR imagery point out the proposed entropy-based segmenters can be more efficient than other well-defined methods, like the clustering by gamma mixture models.

Polarimetric Synthetic Aperture Radar (PolSAR) is one of the most important remote sensing tools. However, PolSAR images are strongly contaminated by a multidimensional interference (called speckle noise), making their processing (e.g. in the classification context) difficult. In terms of structure, multilook PolSAR data follow a definite positive hermitian behaviour and, therefore, require tailored classifiers for their features. Some classic classifiers – such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) – have been yielding unacceptable performance to these data, when applied directly. One justification is because they do not often take into account neither the speckle presence nor properties which are inherent to under-study relieves. This paper addresses a collection of PolSAR divergence-based classifiers, deduced from the normal, skew-normal, t-Student, and skew-t vector models as well as the scaled complex Wishart (SCW) distribution. The last model is a standard supposition to describe multilook PolSAR data, having two parameters: covariance matrix (which is directed to data nature) and number of looks (which controls the speckle noise effect). The considered remainder laws aim to model the main diagonal of these data, known as multivariate intensities. The performance of proposed methods is quantified and compared with those due to the Kullback-Leibler (KL) distance for multivariate normal distribution and to LDA, QDA, KNN, and SVM methods. Experiments with both artificial and real PolSAR data are considered. Results favour optimal Rényi classifiers for an Airborne Synthetic Aperture Radar (AIRSAR) image of San Francisco and the t-Student KL classifier for an SAR image system of the Electromagnetics Institute (EMISAR) image of Foulum.

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 to PolSAR physical formation are sought. In this paper, we propose two new matrix models which arise from applying the stochastic summation approach to PolSAR, called compound truncated Poisson complex Wishart (CTPCW) and compound geometric complex Wishart (CGCW) distributions. These models offer the unique ability to express multimodal data. Some of their mathematical properties are derived and discussed—characteristic function and Mellin-kind log-cumulants (MLCs). Moreover, maximum likelihood (ML) estimation procedures via expectation maximisation algorithm for CTPCW and CGCW parameters are furnished as well as MLC-based goodness-of-fit graphical tools. Monte Carlo experiment results indicate ML estimates perform at what is asymptotically expected (small bias and mean square error) even for small sample sizes. Finally, our proposals are employed to describe actual PolSAR images, presenting evidence that they can outperform other well-known distributions, such as ${\mathcal{W}}_m^{\mathbb{C}}$, ${\mathcal{G}}_m^0$, and ${\mathcal{K}}_m$.

This paper presents a package written in the language R for classifiers based on Naive Bayes and Fuzzy Naive Bayes named FuzzyClass. This R package implements eight fuzzy classifiers, with option for using the classical ones too. An example in which the Fuzzy Gaussian Naive Bayes method is presented.

Assessment methods have been used for identify the quality of procedures performed by human beings. In virtual reality simulators, user’s interaction data can be collected and utilized by Single User Assessment Systems (SUAS) to assess the user’s performance. In particular, some procedures can be considered well performed when the same objective is achieved a number of times with success within a limited universe of attempts. In this case, the data can be modeled by a Hypergeometric distribution. This paper presents the proposal of a new Double Weighted Fuzzy Hypergeometric Naive Bayes (DW-FHyperNB) Network as basis for a SUAS, which can be used in virtual reality simulators. The results showed that this new SUAS was able to achieve better results when compared to other SUAS based on different Naive Bayes Networks. This comparison was performed using SUAS based on Fuzzy Hypergeometric Naive Bayes Network, Classical Hypergeometric Naive Bayes Network, Double Weighted Classical Hypergeometric Naive Bayes Network, Multinomial Naive Bayes Network, Bayes Net, Support Vector Machine, Multilayer Perceptron Neural Network, Radial Basis Network with Gaussian Functions, C4.5 Decision Tree, Decision Table-Naive Bayes, Multinomial Logistic Regression, Simple Cart, and Random Forest was performed. The results obtained showed that the DW-FHyperNB Network produced the best performance, according to the Overall Accuracy Index, Kappa and Tau Coefficients, and diagnostic tests. The new DW-FHyperNB Network can also be utilized for data and image classification, pattern recognition, as well as machine learning applications.