Special Issue "Computational Intelligence in Remote Sensing"
Deadline for manuscript submissions: closed (1 June 2019).
Interests: hyperspectral image analysis; computational intelligence; medical imaging
Special Issues and Collections in MDPI journals
Interests: hyperspectral image analysis; data drift analysis; health care data analysis
Interests: big data; data science; remote sensing; social network analysis
Interests: image processing; robotics; machine vision
The appearance of new and more powerful remote sensing technologies has produced a surge of remote sensing data to be processed for a variety of applications, such as precision agriculture, biomass estimation, fire prevention, forest management, environmental monitoring. Computational intelligence tools for data processing are increasingly being used for pre-processing, enhancement, classification, and construction of thematic maps, change detection, target detection, subpixel resolution analysis, and other general processes. The rediscovery of artificial neural networks with the resurgence of deep learning approaches has injected new vitality in various of the research fields that deal with remote sensing data analysis. Of paramount importance for the development of reproducible science is the availability of data repositories and open source codes that may be used by researchers across the world to confirm or refute claimed results. The open source code has boosted many data science applications, allowing the researchers to work on high-level developments and providing a unified set of tools. In this Special Issue, we emphasize the availability of data and open-source solutions, so that papers may be devoted to describing and sharing such platforms. Besides, we are interested in innovative computational intelligence techniques and algorithms contributing to the state of the art, including deep learning architectures, new bio-inspired optimization techniques, and fuzzy reasoning techniques. We also look for studies presenting techniques dealing with the changing, non-stationary nature of the data considered in time, a main challenge faced by the new generation of remote sensing data analysis tools. Finally, papers describing techniques exploiting various data sources, such as multimodal image fusion or other, are welcome.
Prof. Manuel Graña
Prof. Michal Wozniak
Dr. Sebastian Rios
Dr. Javier de Lope Asiaín
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 papers will be 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. Sensors 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 2000 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.
- Remote sensing data repositories
- Open-source solutions and platforms
- Deep learning and machine learning techniques
- Change detection and data drift
- Classification, target detection, subpixel resolution detection
- Multispectral and hyperspectral images, synthetic aperture
- radar, LIDAR
- Multimodal image fusion
- Applications: precision agriculture, environmental monitoring