From data collection
to final products.

FERN.Lab combines extensive competencies
along the entire remote sensing processing chain.  

Select, find & provide environmental data

Sensor simulations

Development of future earth observation sensors

The development of future earth observation sensors requires detailed knowledge of performance and scope of application.
FERN.Lab develops high quality end-to-end sensor simulation and parameter testing for future remote sensing data.

Commercial data

Use of very high resolution data

Commercial data provides very high spatial resolution data that complements the scale and temporal resolution of open data.
FERN.Lab processes commercial data from a variety of sensors (Planet, Pleiàdes, Worldview, TerraSAR and TanDEM-X).

Open data

Use of free and open data

Open data (Copernicus, Landsat, Envisat) provide dense time series offering new insights into pressing environmental and social issues.
FERN.Lab processes and compiles data stacks for individual applications and long time series analyses.


Diverse support for user applications

Choosing the right data for individual applications requires deep knowledge of data sources and processing techniques.
FERN.Lab support you in finding the right data, taking into account important factors such as availability - temporal repetition rate and dependence on cloud cover, spectral properties, spatial resolution, and cost.
Data preparation & pre-processing

Data homogenization

Application of multi-sensor data

For many applications combining data from different remote sensing satellites provides added value.
FERN.Lab develops data homoenization processing chains for a variety of data sources (Sentinel-2, Landsat, Aster, PlanetScope, EnMap). Processing includes spatial, spectral and radiometric homogenization to make the data compatible and comparable.

Time series analysis

Change and anomaly detection in time series

The high temporal resolution of available earth observation data allows detailed change analyses of our environment.
FERN.Lab extracts the required information from the vast amount of available data to monitor and analyse a multitude of spatio-temporal procceses.

Big data analysis

Technology development

Effectively exploiting big and diverse datasets is a major challenge in the field of earth observation.
FERN.Lab supports the development of new, effective technologies to analyse large, multi-sensor datasets (petabyte scale) and demonstrate their suitability for select applications.
Algorithm development & validation

Optical Remote Sensing

Visible near infrared to short wave infrared wavelengths

Optical remote sensing has provided unprecedented information on a variety of environmental and anthropogenic phenomenon over the last 50 years.  Thanks to the scale, frequency and resolution of optical sensors, key processes such as desertification, deforestation, urbanization and pollution can be observed and monitored.
FERN.Lab provides innovative, state-of-the-art multispectral and hyperspectral data analysis with expertise in both physical-based and machine learning methods for a variety of applications and scales (laboratory, UAV, satellite).

Thermal Remote Sensing

Using radiant temperature to retrieve surface conditions

Thermal remote sensing is complementary to optical and radar remote sensing providing novel information on surface materials and features. Minor temperature variations may signify significant changes in surface properties such as the water balance of plants and soils as well as properties of surface materials.
FERN.Lab is at the forefront of thermal remote sensing data analysis. Thermal data provide information on the water balance of plants and soils as well as properties of surface materials. FERN.Lab provides novel techniques for the analysis of combined thermal and hyperspectral data (Telop HyperCam) as well as thermal and multispectral data.

Radar Remote Sensing

Scanning the Earth’s surface with microwave radiation

Synthetic Aperture Radar or SAR sensors transmit a microwave pulse from an antenna and use the backscattered echo to generate high-resolution images and data products. SAR has the advantage of being weather independent and can be used to infer surface roughness, geometry and dielectric properties (molecular state – solid, liquid, gas).
FERN.Lab is experienced in analysing open and commercial SAR data for classification applications (backscatter, polarimetry and coherence) as well as deformation identification using SAR interferometry (InSAR) and offset tracking. FERN.Lab applies both standard preprocessing workflows as well as machine learning approaches to retrieve required information.
Implementation & operationalization

User training

Provide expert knowledge

FERN.Lab develops and implements demand-based user training on remote sensing methods and in-house products.
The focus is on practical knowledge transfer through an entertaining training concept with practical exercises, tutorials and demos. During the training sessions, users gather knowledge about the potential, limitations, and other characteristics of remote sensing data, methods and products.

GUI development

Use of very high resolution data

FERN.Lab develops user-friendly applications and graphical user interfaces (GUI) for complex scientific remote sensing algorithms.
This makes scientific software accessible and usable for many different user groups.

API development

APIs for smooth interaction between programs

FERN.Lab develops suitable interfaces (APIs) to enable integration of remote sensing data and in-house software with common programs and applications.
Our experts have knowledge to create suitable interfaces (APIs) to link multiple back-end modules in different programming languages and to realize interactions with a front-end service (e.g. web service).


Comprehensive user and product documentation

FERN.Lab creates both user and product documentation for scientific software. Comprehensive documentation not only makes the product easier to understand and use, but it also ensures consistent development and repeatability improving the quality and user experience.
User documentation explains the software in an understandable way to ensure optimal use of the software. While product documentation includes information about about the technical aspects and the development steps of the software.