Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …

Change detection (CD) is one of the most important research areas in remote sensing. With
the fast development of imaging techniques, CD using cross-domain remote sensing images …

Hyperspectral image (HSI) has rich spatial–spectral information, high spectral correlation,
and large redundancy between information. Due to the sparse background distribution of …

Infrared and visible image fusion (IVF) aims to generate a fused image with abundant texture
details and salient thermal radiation targets, which can not only preserve the necessary …

With the aid of transformers, significant progress has been achieved in hyperspectral image
change detection (HSI-CD) in recent times. Nonetheless, most contemporary detection …

Deep learning-based hyperspectral image (HSI) change detection (CD) approaches have a
strong ability to leverage spectral-spatial-temporal information through automatic feature …

Infrared and visible image fusion aims to generate a single fused image, which not only
contains rich texture details, but also beneficial for high-level vision tasks. However, the …

In order to be adapted to high-level vision tasks, several infrared and visible image fusion
methods cascade with the downstream network to enhance the semantic information of …

Convolutional neural networks (CNNs) have performed notably in change detection (CD)
tasks due to their superior learning and automatic feature extraction capabilities. However …

Satellite-based precipitation estimation plays a crucial role in climate change assessment
and water resource management, benefiting from its wide coverage. However, the …