It is a challenge to achieve high compression rates for remote sensing images because they
have rich information and complex backgrounds. Long-range context information can help …

With the increasing volume of high-resolution satellite images, image compression
technology has become a research hotspot in the field of remote sensing image processing; …

Learned image compression has achieved a series of breakthroughs for nature images, but
there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets …

This paper establishes a review of the recent study in the field of hyperspectral (HS) image
compression approaches. Recently, image compression techniques have achieved …

A multispectral image is a three-order tensor since it is a three-dimensional matrix, ie, one
spectral dimension and two spatial position dimensions. Multispectral image compression …

This paper addresses the problem of efficient predictive lossless compression on the
regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a …

This chapter reviews major standards that are fit for remote sensing big data. Two groups of
standards are reviewed: metadata standards and data standards. The International …

The accurate onboard detection of clouds in hyperspectral images before lossless
compression is beneficial. However, conventional onboard cloud detection methods are not …

Hyperspectral images, which record the electro-magnetic spectrum for a pixel in the image
of a scene, often store hundreds of channels per pixel and contain an order of magnitude …

For efficient compression of hyperspectral images, we propose a universal Golomb-Rice
coding parameter estimation method using deep belief network, which does not rely on any …