Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives
Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …
tools and data fusion strategies has recently opened new perspectives for environmental …
Multimodal data fusion for systems improvement: A review
In recent years, information available from multiple data modalities has become increasingly
common for industrial engineering and operations research applications. There have been a …
common for industrial engineering and operations research applications. There have been a …
Infrared and visible image fusion based on target-enhanced multiscale transform decomposition
In this study, we propose a target-enhanced multiscale transform (MST) decomposition
model for infrared and visible image fusion to simultaneously enhance the thermal target in …
model for infrared and visible image fusion to simultaneously enhance the thermal target in …
[HTML][HTML] A survey on semantic communications: technologies, solutions, applications and challenges
Semantic Communication (SC) has emerged as a novel communication paradigm that
provides a receiver with meaningful information extracted from the source to maximize …
provides a receiver with meaningful information extracted from the source to maximize …
Self-supervised SAR-optical data fusion of Sentinel-1/-2 images
The effective combination of the complementary information provided by huge amount of
unlabeled multisensor data (eg, synthetic aperture radar (SAR) and optical images) is a …
unlabeled multisensor data (eg, synthetic aperture radar (SAR) and optical images) is a …
Deep unsupervised embedding for remotely sensed images based on spatially augmented momentum contrast
Convolutional neural networks (CNNs) have achieved great success when characterizing
remote sensing (RS) images. However, the lack of sufficient annotated data (together with …
remote sensing (RS) images. However, the lack of sufficient annotated data (together with …
Self-distillation feature learning network for optical and SAR image registration
Optical and synthetic aperture radar (SAR) image registration is important for multimodal
remote sensing image information fusion. Recently, deep matching networks have shown …
remote sensing image information fusion. Recently, deep matching networks have shown …
Graph relation network: Modeling relations between scenes for multilabel remote-sensing image classification and retrieval
Due to the proliferation of large-scale remote-sensing (RS) archives with multiple
annotations, multilabel RS scene classification and retrieval are becoming increasingly …
annotations, multilabel RS scene classification and retrieval are becoming increasingly …
Environmental resilience through artificial intelligence: innovations in monitoring and management
The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields,
with its applications spanning finance, engineering, healthcare, and more. In recent years …
with its applications spanning finance, engineering, healthcare, and more. In recent years …
Deep metric learning based on scalable neighborhood components for remote sensing scene characterization
With the development of convolutional neural networks (CNNs), the semantic understanding
of remote sensing (RS) scenes has been significantly improved based on their prominent …
of remote sensing (RS) scenes has been significantly improved based on their prominent …