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) and drone remote sensing (DRS) are two paradigms broadening the set of application domains to which CI techniques are applied. With greater data stemming from satellites and drones, 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 and DRS. 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 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 and DRS problems. Artificial Neural Networks, specifically Deep Learning Neural Networks, Spiking Neural Networks, and Extreme Learning Machine, Fuzzy logic as well as the gradient-free optimization techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization Cuckoo Search Algorithms, Firefly Algorithms, etc., play an important role in decision-making and modeling in RS related problems. Also, this symposium deals with different CI techniques for solving RS problems on big data.
The aim of the symposium is to bring together researchers from the academia and industries in the fields of RS and CI.
CIRS invites authors to submit their contributions in the areas including, but not limited to, the following:
CI techniques applied on Satellite/Drone data
- AVHRR, MODIS, VIIRS, LandSat, Sentinel, SAR/POLSAR, Lidar, hyperspectral,multispectral such as 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.
- Short/long term change detection, land-surface phenology, disaster monitoring, forest monitoring, land use and land cover mapping, oil spill detection, ocean surface monitoring, land surface temperature, land surface dynamics, target detection, numerical weather modelling, agriculture monitoring, road extraction, forest fire mitigation, urban sprawl, power line monitoring, etc.
July 10, 2019: Full Paper Submissions
Sept 1, 2019: Notification of acceptance
Oct 1, 2019: Final Version and Early Registration
Dec 6-9, 2019: Conference Dates
Jon Atli Benediktsson
University of Iceland, Iceland
|Jocelyn Chanussot||Grenoble Institute of Technology, France|
|Xiaoyang Zhang||South Dakota State University, USA|
|Shutao Li||Hunan University, China|
|Uttam Kumar||International Institute of Information Technology, India|
|Bharath Aithal||Indian Institute of Technology, India|
|X.S. Yang||Middlesex University, UK|
|P. R. Marpu||Masdar Institute, Khalifa University of Science and Technology, UAE|
|Pedram Ghamisi||Germany Aerospace Center (DLR), Germany|
|Xiuliang Jin||Chinese Academy of Agricultural Sciences, China|
|Yongshuo Fu||Beijing Normal university, China|