Special Issue "New Statistical Approaches for Turning SAR/PolSAR Data into Information"
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 3053
Interests: remote sensing; SAR/PolSAR; speckle; statistical modelling; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: statistical computing; SAR; PolSAR; speckle; information theory; information geometry
In this last decade, research on SAR (Synthetic Aperture Radar) and PolSAR (Polarimetric SAR) systems has received increasing interest, leading to truly innovative applications. Computational capabilities have also supported such development, allowing to better process the large available data provided by the existing SAR and PolSAR satellites and airborne systems.
To transform such daily increasing amount of high-quality, modern, remote sensing data into valuable information, new methods and new strategies are required. In this sense, new statistical models for new high-resolution SAR/PolSAR systems assisting on retrieving land information (soil moisture, cover vegetation, urban areas, ocean surface parameters, target identification) are of maximum interest for both researchers and final users.
For real-time applications, the elaboration of efficient methods to extract significant information from data remains a challenge. This Special Issue focuses on novel techniques regarding the data-to-information process related to SAR/PolSAR systems and on easing their potential applications. It covers a broad and comprehensive series of subjects related to statistical modeling, information theory, machine-learning approaches, data acquisition, and data delivery to users for immediate assimilation. Topics may also include emerging statistical models for signal processing and image interpretation.
For this Special Issue, we invite submissions on, but not limited to, the following topics:
- Statistical models for SAR/PolSAR data
- Modern Classification/Segmentation Methods
- Information Theory for SAR/PolSAR applications
- Statistical signal processing of SAR/PolSAR data
- Machine learning
- Statistical representation of SAR/PolSAR data
- Statistical insights of noise modelling
Dr. Luis Gómez Déniz
Prof. Alejandro C. Frery
Dr. Gui Gao
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Statistical models
- Information theory
- Data representation
- Image interpretation
- Signal processing