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

In the past decades, remote sensing (RS) data fusion has always been an active research
community. A large number of algorithms and models have been developed. Generative …

The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the
spotlight. The combination of high spatial resolution MS images with HS data showing a …

In this article, we propose an efficient convolutional long short-term memory (ConvLSTM)
network with dual-direction transfer for pan-sharpening, termed D2TNet. We design a …

Panchromatic (Pan) sharpening, or pansharpening, refers to the combination of a
multispectral (MS) image and Pan data with a finer spatial resolution. Since the early days of …

In the realm of conventional deep-learning-based pan-sharpening approaches, there has
been an ongoing struggle to harmonize the input panchromatic (PAN) and multi-spectral …

Pansharpening refers to fusing a low-resolution multispectral image (LRMS) and a high-
resolution panchromatic (PAN) image to generate a high-resolution multispectral image …

Carefully curated and annotated datasets are the foundation of machine learning (ML), with
particularly data-hungry deep neural networks forming the core of what is often called …

In latest years, deep learning (DL) has gained a leading role in the pansharpening of
multiresolution images. Given the lack of ground truth data, most DL-based methods carry …

Wasserstein distance (WD) and the associated optimal transport plan have been proven
useful in many applications where probability measures are at stake. In this paper, we …