Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …

Change detection (CD) aims to distinguish surface changes based on bitemporal images.
Since high-resolution (HR) images cannot be typically acquired continuously over time …

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images
is a challenge given the diversity of sensors available and the heterogeneity of forests …

In recent decades, high-resolution (HR) remote sensing images have shown considerable
potential for providing detailed information for change detection. The traditional change …

Abstract Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic
Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce …

Nowadays, our ability to acquire remote sensing data has been improved to an
unprecedented level. We have entered an era of big data. Remote sensing data are clearly …

Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used
in remote sensing applications such as target detection, change detection, and anomaly …

Deep learning has significantly advanced the change detection (CD) in remote sensing (RS)
image with its excellent performance. For CD tasks, there are two critical issues. First, with …

Homogeneous image change detection research has been well developed, and many
methods have been proposed. However, change detection between heterogeneous images …

The very high spectral resolution in hyperspectral images (HSIs) offers an opportunity to
detect subtle land-cover changes. However, the availability of HSIs acquired from different …