Computational Intelligence in Remote Sensing

Dear Colleagues,

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
Guest Editors

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