Remote Sensing Data Fusion as a Strategy to Add Value to Earth Observation Data
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 49418
Special Issue Editors
Interests: remote sensing; artificial neural network; Image; medical image; Based Image Analysis
Interests: image processing, image fusion, data fusion, data analytic and machine learning applied to agriculture water management
Special Issue Information
Dear Colleagues,
The continuous improvements of remote sensors in applications like monitoring and management of the environment, precision agriculture, and security and defenses, involve having large volumes of data that need to be processed to allow the extraction of useful and valuable information. Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. For the particular case of image fusion, multiple images from single or multiple imaging modalities are merged to improve the imaging quality, preserving or enhancing the specific features and eliminating redundant information. In order to improve the value of remotely sensed images, in addition to fuse different modalities, they can be also merged with different types of earth observation data. The aim of this Special Issue is to highlight the latest advances and trends in remote sensing data fusion strategies, images, and any other types of data that can add value to raw Earth observation data.
This Special Issue welcomes articles dedicated to all aspects of multi-sensor (e.g., multi-, hyper-spectral, SAR ...) and temporal remote sensing data fusion, as well as the fusion of remotely sensed images with other kinds of data. Articles may focus on, but are not limited to, novel image fusion algorithms, based on transform domain, machine learning, and other theoretical approaches. Their applications in urban areas, environment, agriculture, and natural resources management in a climate change scenario are also of interest.
Dr. Consuelo Gonzalo-Martín
Dr. Mario Lillo-Saavedra
Dr. Angel M. Garcia-Pedrero
Guest Editors
Manuscript Submission Information
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Keywords
- Image fusion
- Data fusion
- Transformed domain
- Machine learning
- Deep learning
- Multi-resolution analysis
- Remote sensing optical data
- SAR data
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