In recent years, unsupervised feature learning based on a neural network architecture has
become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can …

Recently, the frequency of natural and environmental disasters has increased significantly,
causing constant changes on the Earth's surface. Synthetic Aperture Radar (SAR) data have …

Images obtained from coherent illumination processes are contaminated with speckle. A
prominent example of such imagery systems is the polarimetric synthetic aperture radar …

This paper discusses change detection in SAR time-series. First, several statistical
properties of the coefficient of variation highlight its pertinence for change detection …

This paper explores the problem of change detection in time series of heterogeneous
multivariate synthetic aperture radar images. Classical change detection schemes have …

This paper presents a method for analyzing Synthetic Aperture Radar (SAR) and
polarimetric SAR (PolSAR) image time series based on change detection matrices (CDM) …

In this article, we introduce the wavelet energies correlation screening (WECS), an
unsupervised method to detect spatio-temporal changes on multitemporal SAR images. The …

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

This paper presents a likelihood ratio test based method of change detection and
classification for synthetic aperture radar (SAR) time series, namely NORmalized Cut on …

Rapid urban sprawl and re-construction of old towns have been leading to great changes of
land use in cities of China. To witness short-term urban land use changes, rapid or real time …