OpAiRS - Data Processing and Archiving

The quantitative analysis of imaging spectrometer data requires a dedicated pre-processing and calibration procedure. At DLR software packages are developed, that fulfil the requirements of an operational and semi-automatic pre-processing of HyMap and ROSIS data.

Overview of the Processing Chain

The first part of the generic processing system includes data transcription from system file format to a standardized generic data format to be archived as raw data (level L0). During this step, a first set of data quality indicators based on the sensor performance during data acquisition, as well as sensor-specific housekeeping data required for long-term sensor monitoring are calculated and archived.

After that, data is processed to at-sensor radiance (L1) based on the laboratory calibration. The validation of sensor calibration is carried out using in-flight calibration based on test flights and ground reference measurements. For HyMap data, the system correction is carried out by HyVista (Cocks et al., 1998). For the experienced user, the L1 data product including all required metadata and data quality reports is available.

In order to process L2 products, parametric geocoding and/or atmospheric correction can be applied. The software packages used are ORTHO for orthographic rectification (Müller, R. et al., 2005), and ATCOR (Richter and Schläpfer, 2002) for atmospheric / topographic correction. Both programs were developed and customized at DLR for the fully automated processing of large datasets. The necessary digital elevation model (DEM) is automatically retrieved from the digital elevation database W42 at DLR (Roth et al., 2002). As an option, the PARGE software package (Schläpfer and Richter, 2002) can also be used for the semi-automated geocoding.

For L2 data, the processing can be customized (e.g., interpolation method, processing sequence, selection of atmospheric parameters, data smoothing). The L2 data product is also distributed with an extended data quality report, which includes details on the processing and a characterization of the image data.

The whole processing chain is embedded in DLR's multi-mission Data Information and Management System DIMS (Mikusch et al., 2000), an automated processing and archiving environment established for the handling and distribution of satellite data.

References

COCKS, T.; JENSSEN, T.; STEWARD, A.; WILSON, I.; SCHIELDS, T. (1998): The HyMap Airborne Hyperspectral Sensor: the System, Calibration, and Performance. In: Proceedings of the First EARSeL Workshop on Imaging Spectroscopy, ZÜrich.

BACHMANN, M.; HABERMEYER, M.; HOLZWARTH, S.; RICHTER, R.; MÜLLER, A. (2007): Including Quality Measures in an Automated Processing Chain for Airborne Hyperspectral Data. In: Proceedings of the 5th EARSeL Workshop on Imaging Spectroscopy, Bruges.

HABERMEYER, M.; MÜLLER, A.; HOLZWARTH, S.; RICHTER, R.; MÜLLER, R.; BACHMANN, M.; SEITZ, K.-H.; SEIFERT, P.; STROBL, P. (2005): Implementation of the Automatic Processing Chain for ARES. In: Proceedings of the 4th EARSeL Workshop on Imaging Spectroscopy. Warsaw.

MÜLLER, R.; HOLZWARTH, S.; HABERMEYER, M.; MÜLLER, A. (2005): Ortho Image Production within an Automatic Processing Chain for Hyperspectral Airborne Scanner ARES. In: Proceedings of EARSeLWorkshop 3D-Remote Sensing, Porto.

SCHLäPFER, D.; RICHTER, R. (2002): Geo-atmospheric Processing of Airborne Imaging Spectrometry Data Part 1: Parametric Orthorectification. International Journal of Remote Sensing, 23(13), pp. 2609-2630.

RICHTER, R.; SCHLäPFER, D. (2002): Geo-Atmospheric Processing of Airborne Imaging Spectrometry Data, Part 2: Atmospheric / Topographic Correction. International Journal of Remote Sensing 23(13), pp. 2631-2649.

ROTH, A.; KNÖPFLE, W.; STRUNZ, G.; LEHNER, M.; REINARTZ, P. (2002): Towards a Global Elevation Product: Combination of Multi-Source Digital Elevation Models. In: Proc. of the Joint International Symposium on Geospatial Theory, Processing and Applications, Ottawa.