Remote sensing technology has penetrated all the natural resource segments as it provides
precise information in an image mode. Remote sensing satellites are currently the fastest …

Change detection based on remote sensing (RS) images has a wide range of applications
in many fields. However, many existing approaches for detecting changes in RS images with …

This paper describes new extensions to the previously published multivariate alteration
detection (MAD) method for change detection in bi-temporal, multi-and hypervariate data …

In this chapter, a new unsupervised image clustering approach which is based on the
particle swarm optimization (PSO) algorithm is presented. The algorithm finds the centroids …

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 …

The most commonly used features in unsupervised change detection are spectral
characteristics. Traditional methods describe the degree of the change between two pixels …

In this paper, we propose a novel unsupervised change detection method of remote sensing
(RS) images based on a unified framework for weighted collaborative representation (WCR) …

Principal component analysis (PCA) is often used to detect change over time in remotely
sensed images. A commonly used technique consists of finding the projections along the …

A new type of artificial neural network is used to identify different crops and ground elements
from hyperspectral remote sensing data sets. These networks incorporate Gaussian …

Change detection is a central task in the field of remote sensing. Detection of anthropogenic
or natural impacts on landcover is essential for many environmental studies. On the regional …