A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

A wide range of methods for analysis of airborne-and satellite-derived imagery continues to
be proposed and assessed. In this paper, we review remote sensing implementations of …

This paper introduces the use of single-layer and deep convolutional networks for remote
sensing data analysis. Direct application to multi-and hyperspectral imagery of supervised …

Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …

The technological evolution of optical sensors over the last few decades has provided
remote sensing analysts with rich spatial, spectral, and temporal information. In particular …

Earth observation through remote sensing images allows the accurate characterization and
identification of materials on the surface from space and airborne platforms. Multiple and …

Land-cover mapping in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

This paper addresses change detection in multitemporal remote sensing images. After a
review of the main techniques developed in remote sensing for the analysis of multitemporal …

This paper shows how web usage mining can be applied in e‐learning systems in order to
predict the marks that university students will obtain in the final exam of a course. We have …

In this paper, we study the problem of feature extraction for knowledge transfer between
multiple remotely sensed images in the context of land-cover classification. Several factors …