Recent Advances of Deep Learning Technology in Remote Sensing Image Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 4418
Special Issue Editors
Interests: applied deep learning; applied machine learning; signal and image processing; automatic speech recognition; multi-modal data fusion
2. Laboratoire d’Iformatique et Systèmes, Équipe de modélisation géométrique (G-Mod), Aix-Marseille Université, 13200 Arles, France
Interests: pansharpening; data fusion; multidimensional signal processing; digital image processing; machine and deep learning applications
Interests: deep learning; image fusion; statistical signal processing; image enhancement; classification; detection; tracking
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Special Issue Information
Dear Colleagues,
Image fusion consists of efficiently combining data from different sensors/sources for better interpretation and visualization. This technique has been widely studied and explored in remote sensing over the last few decades. The fused product has been used for several practical applications, including object tracking, land cover classification, and anomaly detection.
Conventional methods suffer from performance reduction in consequence of often unrealistic hypotheses. Recently, deep learning booming has had a remarkable impact on research. Fast computing devices like graphics processing units (GPUs) have also led to the enhanced efficiency of numerous mathematical methods, including very deep learning architectures for complicated tasks.
Although deep learning models have been widely used in remote sensing image fusion, there are still many rooms for improvement. The aim of this Special Issue is to focus on future directions of remote sensing image fusion through most recent advancements in deep learning models.
In particular, we are considering submissions to the following concepts:
- Multi-objective deep learning models for remote sensing image fusion;
- Deep learning-based image fusion with attention mechanism;
- Multi-source image fusion at sensor/pixel/decision level;
- Pixel- and feature-based fusion for classification;
- Multi-temporal image fusion / target detection;
- Change detection using multispectral/hyperspectral image fusion;
- Convolutional neural networks for image fusion;
- Benchmarks for quality assessment;
- Multispectral/hyperspectral image fusion ;
- Multispectral/panchromatic image fusion.
Dr. Arian Azarang
Dr. Hind Hallabia
Dr. Gemine Vivone
Guest Editors
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Keywords
- deep learning
- remote sensing image fusion
- applied machine learning for remote sensing image fusion
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