Occurrence of mixed pixels in remote sensing images is a major problem particularly at
coarse spatial resolutions. Therefore, sub-pixel classification is often preferred, where a …

Soft classification techniques avoid the loss of information characteristic to hard
classification techniques when handling mixed pixels. Sub‐pixel mapping is a method …

High-resolution images have a wide range of applications in image compression, remote
sensing, medical imaging, public safety, and other fields. The primary objective of super …

Spatial resolution is a crucial indicator for measuring the quality of hyperspectral imaging
(HSI) and obtaining high-resolution (HR) hyperspectral images without any auxiliary …

Landscape pattern represents a key variable in management and understanding of the
environment, as well as driving many environmental models. Remote sensing can be used …

Single image super-resolution (SISR) is the task of inferring a high-resolution image from a
single low-resolution image. Recent research on super-resolution has achieved great …

Identification of tree crowns from remote sensing requires detailed spectral information and
submeter spatial resolution imagery. Traditional pixel-based classification techniques do not …

The random function theory forms the basis of geostatistics and allows modelling of the
uncertainty associated with spatial estimation and simulation. Remote sensing involves …

This paper explores the effects of class separability in Markov-random-field-based
superresolution mapping (SRM). We propose to account for class separability by means of …

Many satellite images have a coarser spatial resolution than the extent of land cover
patterns on the ground, leading to mixed pixels whose composite spectral response consists …