EO mission development and data processing

Our group focuses on the development of future EO sensors and state of the art data pre-processing algorithms. Our activities cover a broad spectrum of research fields such as hyperspectral sensors, sensor end-to-end simulations, night illumination, radiative transfer, atmospheric correction, big data processing, as well as geometric fusion of hyperspectral and Lidar data.

Environmental Mapping and Analysis Program

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral mission, scheduled for launch in 2018. The primary goal of EnMAP is to offer accurate, diagnostic information on the state and evolution of terrestrial ecosystems on a timely and frequent basis, and to allow for a detailed analysis of surface parameters with regard to the characterization of vegetation canopies, rock/soil targets and coastal waters on a global scale. EnMAP is designed to record bio-physical, bio-chemical and geo-chemical variables to increase our understanding of biospheric /geospheric processes and to ensure the sustainability of our resources.

GeoMultiSens – Scalable Multi-Sensor Analysis of Remote Sensing Data

GeoMultiSens exploits current scientific and technological advances in Big Data infrastructures, parallel computing environments, Visual Analytics and links them with the potential of satellite remote sensing data to address global challenges such as deforestation, loss of biodiversity, and mega cities. 

Night Illumination

Artificial light is a unique signature of human activity, with strong correlations to population, economic development level, and electrification rate. It is also a form of global change that remains poorly understood, with consequences for ecosystem services and loss of biodiversity. The spectral, spatial, and temporal patterns of artificial light emission of cities are examined.

Processing and fusion of hyperspectral and LIDAR data

Airborne hyperspectral data require careful processing by utilizing automated, consistent, highly flexible and robust methods. The fusion of passive hyperspectral and active LIDAR sensor characteristics represents a current research topic because it enables a comprehensive object characterization by integrating a high spectral with a high spatial resolution. All synergies in terms of geometric and spectral data alignment as well as process refinement are utilized to improve data quality and information content.


Profile photo of  Dr. Karl Segl

Dr. Karl Segl
Remote Sensing

Building A 17, room 20.15
14473 Potsdam
tel. +49 331 288-1193