With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a
low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high …

Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN)
data aimed at generating an outcome with the same spatial resolution of the PAN data and …

Machine learning (ML) is influencing the literature in several research fields, often through
state-of-the-art approaches. In the past several years, ML has been explored for …

The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired
multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called …

By integrating the information contained in multiple images of the same scene into one
composite image, pixel-level image fusion is recognized as having high significance in a …