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

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …

Over the past decade, the incorporation of spatial information has drawn increasing attention
in multispectral and hyperspectral data analysis. In particular, the property of spatial …

High temporal and spatial resolution optical image time series have been proven efficient for
crop type map** at the end of the agricultural season. However, due to cloud cover and …

Change detection (CD) is among the most important applications in remote sensing (RS)
that allows identifying the changes that occurred in a given geographical area across …

This book discusses the fundamentals of land-use and land-cover characterization,
map**, and monitoring using remote sensing technology. After covering the basic …

Markov models represent a wide and general family of stochastic models for the temporal
and spatial dependence properties associated to 1-D and multidimensional random …

This paper presents a new approach for hyperspectral image classification exploiting
spectral-spatial information. Under the maximum a posteriori framework, we propose a …

With the growing research on image segmentation, it has become important to categorise
the research outcomes and provide readers with an overview of the existing segmentation …

The multitemporal classification of remote sensing images is a challenging problem, in
which the efficient combination of different sources of information (eg, temporal, contextual …