Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms
for Python, Fourth Edition, is focused on the development and implementation of statistically …

Remote sensing image change detection remains a challenging task. Most existing
approaches are based on fully supervised learning, but labeled data are so scarce for …

Urban sprawl is a ubiquitous with a complex network of driving forces and human and
natural impacts on various scales of the coupled human-environment urban system. In …

By 2050, two-third of the world's population will live in cities. In this study, we develop a
framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia …

Change detection based on satellite images acquired from an area at different dates is of
widespread interest, according to the increasing number of flood-related disasters. The …

Aeolian processes in drylands often transcend into sand encroachment, a common form of
land degradation. Highly reflective desert features, hence sandy areas, often cause spectral …

The analysis of multi-temporal remote-sensing images is one of the main applications in
Earth's observation and monitoring. In this paper, we present a Matlab toolbox for change …

Change detection techniques play an essential role in Remote Sensing applications, such
as environmental monitoring, governmental planning, and studies of areas affected by …

Forest inventories (FIs) are increasingly supported by auxiliary variables (X) with a
formulated link to a target variable (Y). The tenet forwarded here is that with simple single …

This study combines spaceborne multitemporal and hyperspectral data to analyze the
spatial distribution of surface evaporite minerals and changes in a semi-arid depositional …