Multispectral remote sensing images are the primary source in the land use and land cover
(LULC) monitoring. This is achieved by LULC classification and LULC change detection …

Semi-supervised learning (SSL) aims to exploit the potential of unlabeled data to enhance
model performance, which makes it suitable for addressing the challenge of limited labeled …

Synthetic aperture radar (SAR) images have been applied in disaster monitoring and
environmental monitoring. With the objective of reducing the effect of noise on SAR image …

In order to more efficiently mine the features of polarimetric synthetic aperture radar
(PolSAR) and establish a more appropriate classification model, this article proposes an …

With the development of Earth observation programs, more and more multi-temporal
synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore …

In low-illumination environments such as at night, due to factors such as the large monitoring
field of an eagle eye, short sensor exposure time, and high-density random noise, the video …

The visualization of riverbeds and surface facilities on the banks is crucial for systems that
analyze conditions, safety, and changes in this environment. Hence, in this paper, we …

The intrinsic noise of synthetic aperture radar (SAR) images has a big influence to the image
processing performance, especially in change detection (CD). Image denoising is an …

Change detection is a key problem for many remote sensing applications. In this paper, we
present a novel unsupervised method for change detection between two high-resolution …

Coregistration of high-resolution optical and synthetic aperture radar (SAR) images is still an
ongoing problem due to different imaging mechanisms of two kinds of remote sensing …