The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid ...

The problem of visualizing large volumetric datasets is appealing for computation on the GPU. Nevertheless, the design of GPU volume rendering solutions must deal with the limited available memory in a graphics card. In ...

Image registration is a common operation in any type of image processing, specially in remote sensing images. Since the publication of the scale–invariant feature transform (SIFT) method, several algorithms based on feature ...

In this article, we present a desktop application for the analysis, reference data generation, registration, and supervised spatial-spectral classification of hyperspectral remote-sensing images through a simple and intuitive ...

Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms were not really devoted to real-time performance, ...

Hyperspectral images contain a great amount of information which can be used to more robustly register such images. In this article, we present a phase correlation method to register two hyperspectral images that takes ...

Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase ...

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem ...

Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training ...