The Sentinel-1 satellites provide the formerly unprecedented combination of high spatial
and temporal resolution of dual polarization synthetic aperture radar data. The availability of …

Contrastive self-supervised learning (CSSL) is a promising method for extracting effective
features from unlabeled data. It performs well in image-level tasks, such as image …

Convolutional neural networks (CNNs) have been widely used in change detection of
synthetic aperture radar (SAR) images and have been proven to have better precision than …

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

Remote sensing has been widely employed for monitoring land cover and usage by change
detection techniques. In this paper, we cope with the early detection of the first signs of …

Ship detection in complex environment is a challenging task due to strong background
inferences, for which various deep-learning-based methods have been proposed. However …

Deforestation poses a significant global environmental challenge with far-reaching
consequences for biodiversity, climate change, and livelihoods. In this context, applying …

In recent years, various deep learning based methods have been successfully developed for
change detection, such as Convolutional Neural Network (CNN) based U-Net and its …

Change detection monitoring on multi-temporal remote sensed images is a persistent
methodological challenge where the Dempster-Shafer, or evidence, Theory (DST) has been …

Forest monitoring is a major concern today due to climate changes, conservation of fauna
and flora and to the lack of water. Therefore, several environmental monitoring techniques …