This book provides a thorough introduction to the use of remotely sensed data for wetland
classification and mapping, as well as information on the latest technological advancements …

This letter presents a framework of composite kernel machines for enhanced classification of
hyperspectral images. This novel method exploits the properties of Mercer's kernels to …

Aim To produce a statistical stratification of the European environment, suitable for stratified
random sampling of ecological resources, the selection of sites for representative studies …

A new algorithm for exploiting the nonlinear structure of hyperspectral imagery is developed
and compared against the de facto standard of linear mixing. This new approach seeks a …

Earth observation is the field of science concerned with the problem of monitoring and
modeling the processes on the Earth surface and their interaction with the atmosphere. The …

Mapping tools are needed to document the location and extent of Phragmites australis, a tall
grass that invades coastal marshes throughout North America, displacing native plant …

We propose the use of support vector machines (SVMs) for automatic hyperspectral data
classification and knowledge discovery. In the first stage of the study, we use SVMs for crop …

Hyperspectral remote sensing provides information related to surface material
characteristics that can be exploited to perform automated detection of targets of interest and …

The spectral features in hyperspectral imagery (HSI) contain significant structure that, if
properly characterized, could enable more efficient data acquisition and improved data …

Very high resolution hyperspectral data should be very useful to provide detailed maps of
urban land cover. In order to provide such maps, both accurate and precise classification …