The compression of hyperspectral images (HSIs) has recently become a very attractive issue
for remote sensing applications because of their volumetric data. In this paper, an efficient …

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 …

Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into
sparse coefficients with very few nonzero entries, which can efficiently be used for …

Improved sensor characteristics are generally assumed to increase the potential accuracy of
image classification and information extraction from remote sensing imagery. However, the …

The Encyclopedia of Image Processing presents a vast collection of well-written articles
covering image processing fundamentals (eg color theory, fuzzy sets, cryptography) and …

The existing lossy compression algorithms play an important role in reducing the cost of
storage equipment and bandwidth for hyperspectral (HS) application. However, none of the …

The access and distribution convenience of public networks opens a considerable content
security threat when sending, receiving, and using multimedia information. In this paper, a …

A task of classification of multichannel remote sensing images compressed in a lossy
manner is considered. It is recalled that lossy compression usually leads to reduction of …

The compression of hyperspectral images (HSIs) has recently become a very attractive issue
for remote sensing applications because of their volumetric data. An efficient method for …

In the past, scientific data have been almost exclusively compressed by means of lossless
methods, in order to preserve their full quality. However, more recently, there has been an …