Abstract Cellular Automata (CA) & Markov-Chain modeling are concepts that are utilized in
numerous branches of science. Powerful as they are independently, these two theoretical …

This paper is a brief survey of advance technological aspects of Digital Image Processing
which are applied to remote sensing images obtained from various satellite sensors. In …

In the traditional remote sensing image recognition, the traditional features (eg, color
features and texture features) cannot fully describe complex images, and the relationships …

Sparse representation has been widely used in the field of remote sensing image super-
resolution (SR) to restore a high-quality image from a low-resolution (LR) image, eg, from …

Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure
of the image content, eg, by assigning a semantic label to every pixel of the image. With the …

Texture recognition is one of the most important tasks in computer vision and, despite the
recent success of learning-based approaches, there is still need for model-based solutions …

Remote Sensing is a multi-disciplinary technique for image acquisition and measurement of
information. Remote sensing analysis paved way for satellite image classification which …

Classification is an important task of machine learning for solving a wide range of problems
in conforming patterns. In the literature, machine learning algorithms dealing with non …

The basic application of remote sensing is classifying surface objects in images. Traditional
pixel-based or object-based classification methods are poorly suited to very high-resolution …

Background Fires in the boreal forest occur with natural frequencies and patterns. Burned
area (BA) is an essential variable in assessing the impact of climate change in boreal …