IEEE Symposium on Computational Intelligence in Remote Sensing (IEEE CIRS)

This symposium emphasis on the development of computational intelligence (CI) techniques for solving remote sensing (RS) problems. RS in particular satellite remote sensing (SRS), drone remote sensing (DRS) and Near-surface Remote Sensing (NRS) are three paradigms broadening the set of application domains to which CI techniques are applied. With greater data stemming from satellites, drones and phenocam, there is a need to build intelligent data processing systems using CI for effective and efficient ways of solving a wide range of problem areas in SRS, DRS and NRS. The system is said to be intelligent if it can perceive their goals, automatic in processing, learn from the environment and past experiences, and adapt to accommodate the fast-changing environments and goals. Each task in an intelligent system is interesting and valuable in its own right, but building such system can facilitate a fundamental shift in the way we see them for solving complex SRS, DRS and NRS problems. Artificial Neural Networks, specifically Deep Neural Networks, Spiking Neural Networks, Online Learning and Continual Lifelong Learning, Fuzzy logic as well as the gradient-free optimisation techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Firefly Algorithms, etc., play an important role in decision-making and modelling in RS related problems. Also, this symposium deals with different CI techniques for solving RS problems on big data. This symposium aims to bring together researchers from the academia and industries in the fields of RS and CI.

Topics: CIRS invites authors to submit their contributions in the areas including, but not limited to, the following:

  • CI techniques applied on Satellite/Drone/Phenocam data
    • AVHRR, MODIS, VIIRS, LandSat, Sentinel, SAR/POLSAR, Lidar, hyperspectral, multispectral (IKONOS, QuickBird), visual RGB, thermal IR etc.
  • CI based processing tools
    • Image enhancement, speckle filtering, image registration, spectral unmixing, spatio-spectral fusion, dimensionality reduction, band selection, image classification, image clustering, image segmentation, regression techniques, spectral-spatial methods, etc.
  • CIRS applications
    • Short/long term change detection, disaster monitoring, land-surface phenology,  climate change, forest monitoring, land use and land cover mapping, oil spill detection, ocean surface monitoring, land surface temperature, land surface dynamics, target detection, weather modelling, agriculture monitoring, road extraction, forest fire mitigation, urban sprawl, power line monitoring, etc.

Symposium Chairs

Programme Committee

Antonio J. Plaza, University of Extremadura, Spain
Bharath Aithal, Indian Institute of Technology, India
Ferdaus Md Meftahul, Agency for Science, Technology and Research, Singapore
Indiramma M, BMS College of Engineering Bangalore, India
Jocelyn Chanussot, Grenoble Institute of Technology, France
Magnús Örn Úlfarsson, University of Iceland, Iceland
Pedram Ghamisi, Helmholtz Institute Freiberg for Resource Technology, Germany
Rajdeep Dutta, Agency for Science, Technology and Research, Singapore
Suresh Sundaram, Indian Institute of Science, India
Uttam Kumar, International Institute of Information Technology, India
Xiaoyang Zhang, South Dakota State University, USA
X.S. Yang, Middlesex University, UK
Yongshuo Fu, Beijing Normal University, China