With the recent technological developments in our current information age, there is a huge amount of data being generated and their volume is exponentially increasing. Also, the thirst for managing more data to enhance the observation and monitoring of the Earth has triggered new satellite sensors and platforms over time. Yet, unfortunately, these generated data are standardized and harmonized only partially due to several challenges including the number of sensor types and data. Also, over time, the accumulated data from diverse sources are a rich repository of information, but difficult to investigate and analyze globally.
Data fusion has become a popular multidisciplinary approach that combines data from multiple sources to improve the global potential value and interpretation performance and produce a high-quality final representation of the data. Fusion techniques are useful for various applications like object detection, recognition, identification and classification, object tracking, change detection, decision making, etc. And a remarkable improvement over conventional probabilistic data fusion techniques is coming out of machine learning (ML) techniques, which include strong computing and predicting abilities. Despite the fast development, data fusion approaches remain challenging for remote sensing applications due to various requirements, landscape complexity, temporal, spatial, and spectral variations, etc. within the input dataset.
This special issue focuses on intelligent and advanced signal processing techniques to overcome the DF challenges in ML-based remote sensing applications. Indeed, advanced signal processing solutions can be researched to meet DF challenges for RS applications especially when ML techniques are applied. Both theoretical and experimental studies are welcome, particularly papers with good technical insights on the intelligent and advanced signal processing techniques for RS applications.
Topics of interest include (but are not limited to):
- Application scenarios for ML-based data fusion
- Cross-sensors & Transfer learning
- Data fusion of distributed sensors
- Data fusion on autonomous systems
- Data fusion model performance evaluation
- Detection, classification and segmentation on multi-sensor data
- Image analysis in big dataIntegration of data fusion
- Machine learning challenges in data fusion
- Multi-sensor data processing for image registration
- Multisource data fusion on hyperspectral and super-resolution images
- Time series remote sensing data processing
- Time series learning with scarce or low-quality remote sensing data
- Sensor Applications on Agriculture, Marine, Forest, Geohazard and Military field
Manuscript due: 31 December 2022
First review completed: 31 January 2023
Revised manuscript due: 28 February 2023
Second review completed: 31 March 2023
Final manuscript due: 30 April 2023
Manuscript submission due: 31st December 2022
Revised paper submission: 25th January 2022
Final decision: 28th February 2023
Lead Guest Editor:
Silvia Liberata Ullo, University of Sannio, Benevento, Italy
Parameshachari B.D. (Bidare Divakarachari), Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India
Issaak Parcharidis, Harokopio University, Athens, Greece
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Intelligent and Advanced Signal Processing (IASP) Techniques for Data Fusion (DF) in Machine Learning (ML) Based Remote Sensing (RS) Applications'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
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