High-resolution image is referred as high-dimensional data space as each image data is
organized into two-dimensional pixel values in which each pixel consists of its respective …

In this study, we tested the effectiveness of stand age, multispectral optical imagery obtained
from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data …

Hyperspectral images are often contaminated with noise which degrades the quality of data.
Recently, tensor robust principal component analysis (TRPCA) has been utilized to remove …

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

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 …

With the development of remote sensing technology, spatial and spectral resolutions of
hyperspectral images have become increasingly dense. In order to overcome difficulties in …

Simultaneous sparse representation can transform the correlated spectral signatures of
hyperspectral pixel matrixes into sparse coefficients. It can be very efficient in the …

In the wild, wireless multimedia sensor network (WMSN) communication has limited
bandwidth and the transmission of wildlife monitoring images always suffers signal …

Traditional hyperspectral imaging technique obtains numerous hyperspectral images (HSIs)
with hundreds of spectral bands, leading to high cost in data acquisition, transmission, and …

This paper presents a pairwise KLT-based compression algorithm for multispectral images.
Although the KLT has been widely employed for spectral decorrelation, its complexity is high …