Data, which lie in the space P m, of m× m symmetric positive definite matrices,(sometimes
called tensor data), play a fundamental role in applications, including medical imaging …

We present a simple method to approximate the Fisher–Rao distance between multivariate
normal distributions based on discretizing curves joining normal distributions and …

Remote sensing image scene classification, which consists of labeling remote sensing
images with a set of categories based on their content, has received remarkable attention for …

This paper deals with parametric techniques for the description of texture on very high
resolution (VHR) remote sensing images. These techniques focus on the property of …

The study of P m, the manifold of m× m symmetric positive definite matrices, has recently
become widely popular in many engineering applications, like radar signal processing …

This paper presents coding methods used to encode a set of covariance matrices. Starting
from a Gaussian mixture model adapted to the log-Euclidean or affine invariant Riemannian …

This paper introduces a generalization of the Fisher vectors to the Riemannian manifold.
The proposed descriptors, called Riemannian Fisher vectors, are defined first, based on the …

This paper presents an overview of coding methods used to encode a set of covariance
matrices. Starting from a Gaussian mixture model (GMM) adapted to the Log-Euclidean (LE) …

The new generation of remote sensing imaging sensors enables high spatial, spectral and
temporal resolution images with high revisit frequencies. These sensors allow the …

In this communication, we derive a new intrinsic Cramér-Rao bound for both parameters and
observations lying on Lie groups. The expression is obtained by using the intrinsic …