IAM4MARS: Intelligent Automated Methods for Monitoring Agriculture with Remote Sensing
For monitoring agriculture in Europe, an unsupervised automated method (with limited interaction) is developed based on advanced similarity criteria utilizing spectral/spatial characteristics and on manifold learning techniques for clustering large data sets of very-high resolution images.
Spectral clustering has ability to extract clusters with distinct characteristics without using a parametric model in expense of high computational cost. To utilize its advantages in large datasets where it is infeasible, ASC methods apply spectral clustering on a reduced set of points (data representatives) selected by sampling/quantization.The SFT will provide a fast and accurate approach for assessment of agricultural systems at the community level, which is currently done by expert image analysis. The contributions are threefold: i) advanced similarity criteria for approximate spectral clustering (ASC); ii) ensemble methods for ASC; iii) monitoring agriculture with proposed methods. These contributions produce effective clustering not only for remote sensing images but also other large datasets.
See also: https://smart-akis.com/SFCPPortal/#/app-h/technologies?techid=143
Smart-AKIS: European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research
in Smart Farming Technology
Ongoing | 2016-2018
in Smart Farming Technology
- Main funding source
- Horizon 2020 (EU Research and Innovation Programme)
- Geographical location
- Greece