About the Project

SENTISIMULAT

Objectives

  • To develop a LC-LU classification scheme to support EU and national decision making processes and efficient policy accounting (CAP, subsidies, biodiversity conservation, regional development, climate adaptation, flood risk management, ecological corridors, etc.);
  • To adapt IES’s flying laboratory to perform a simulation of Sentinel-2 imagery;
  • To develop a LC-LU classification algorithm for Sentinel-2 data and implement into a prototype by using experience and products developed in the frame of other EU-funded projects (e.g. Geoland 2, HLANDATA);
 
  • To verify the classification results with high-resolution airborne RGB images, obtained simultaneously with hyperspectral data and in-situ ground measurements for difficult or questionable sites or classes;
  • To perform a feasibility study on fusion of Sentinel-2 satellite images (simulated by airborne sensors in 2015, and obtained from the satellite after it is launched) with  airborne hyperspectral data and Landsat-8 Thermal Infrared (TIR) bands to extract more sub-classes;
  • After the satellite’s launch in 2015, to assess the influence of atmosphere on satellite data signal by comparing the real Sentinel-2 data and results of its simulation by airborne sensors.

 

Solutions

IES is the main contractor. The airborne remote sensing data will be collected with IES’s Airborne Environmental Surveillance and Monitoring System ARSENAL. For verification of LC-LU classes an image of high spatial resolution (0.2-4m), covering the same swath, will be acquired simultaneously with the hyperspectral data.

The observation will be fulfilled by two hyperspectral airborne imaging sensors (infrared light) and a high resolution RGB camera (visible light) which are embedded in the ARSENAL. The spectral band of the two hyperspectral sensors will be applied to simulate the Sentinel-2 bands and the maximum swath and required spatial resolution both for hyperspectral and RGB sensors will be reached by careful data collection planning.

As a result of the project, IES will develop a LC-LU classification algorithm for the Sentinel-2 data.

Contacts