Abstract Hyperspectral Image Processing (HSIP) is an essential technique in remote
sensing. Currently, extensive research is carried out in hyperspectral image processing …

A maximum-likelihood method for estimating hyperspectral sensors random noise
components, both dependent and independent from the signal, is proposed. A hyperspectral …

Hyperspectral data lossy compression has not yet achieved global acceptance in the remote
sensing community, mainly because it is generally perceived that using compressed images …

Remote sensing applications have gained in popularity in recent years, which has resulted
in vast amounts of data being produced on a daily basis. Managing and delivering large sets …

Urban greenspace is important for the health of cities. Up-to-date databases and information
are vital to maintain and record growth in cities. Despite detailed mapping of urban land …

In this chapter we describe methods how to compress spectral imaging data. Normally the
spectral data is presented as spectral images which can be considered as generalizations of …

Multitemporal hyperspectral images are gaining an ever-increasing importance revealed by
the ambition of the remote sensing community to develop new generation of sensors …

In this paper, we analyze the effect of spatial and spectral compression on the performance
of statistically based retrieval. Although the quality of the information is not completely …

The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop
satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI …

Mediterranean forests are characterized by spatiotemporal heterogeneity that is associated
with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote …