Hyperspectral imaging is an indispensable technology for many remote sensing
applications, yet expensive in terms of computing resources. It requires significant …

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 …

Predictive lossy compression has been shown to represent a very flexible framework for
lossless and lossy onboard compression of multispectral and hyperspectral images with …

With the rapid development of hyperspectral remote sensing technology, the spatial
resolution and spectral resolution of hyperspectral images are continually increasing …

In the field of satellite imaging, effectively managing the enormous volumes of data from
remotely sensed hyperspectral images presents significant challenges due to the limited …

Hyperspectral imaging (HSI) technology has been used for various remote sensing
applications due to its excellent capability of monitoring regions-of-interest over a period of …

The Consultative Committee for Space Data Systems (CCSDS) has issued several data
compression standards devised to reduce the amount of data transmitted from satellites to …

Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless
compression techniques for remote sensing data. This article presents the first regression …

Hyperspectral image compression is an important task, where the aim is to store or transmit
data in an efficient way. Hyperspectral images are mostly captured by a sensor system that …

An efficient lossless compression scheme for hyperspectral images using conventional
recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The …