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OrthoServ Project

OrthoServ

Sarmap (CH), ETH Zurich (CH), MEEO (IT)

The correct use of Earth Observation data implies a rigorous data calibration in geometric, radiometric - and for SAR systems - polarimetric, and interferometric terms, in particular if the primary goals are:

  • To analyze multi-temporal images acquired from the same sensor;
  • To analyze images acquired from different sensors;
  • To generate indexes;
  • To infer bio- or geo-physical parameters.

Despite the large effort over the past two decades of the different calibration/validation working groups, the quality of pre-processed images - i.e. the generation of co-registered and/or ortho-rectified radiometrically calibrated products - still remains today questionable. The main reasons are:

  • Inappropriate models (often limited to empirical models);
  • Limitations of software tools;
  • Inappropriate input images;
  • Inappropriate sensor parameters;
  • Inaccurate platform parameters;
  • Inaccurate Ground Control Points (GCP) or Tie Points (TP);
  • Inaccurate Digital Elevation Model (DEM);
  • Limited user's knowledge (extended to service providers, in some cases).

The consequence is that Earth Observation applications, in general, are often hindered due to an improper data pre-processing.

 

Project a Glance

In order to quantitatively assess the geometric and radiometric quality of ortho-rectified and co-registered products of key service providers, and the algorithm suitability of selected ESA projects (NEST and MIR-E in particular), a customized Quality Assessment Tool (QAT) based on a solid methodology has been developed. The main characteristics of this QAT are:

  • It supports all operational SAR and the main Optical spaceborne sensors with a spatial resolution better than 30 meters;
  • It supports the key products of the selected sensors;
  • It supports the geometric analysis of single- and multi-date images;
  • A database stores all configuration settings, reference and nominal parameters and Ground Control Points / Tie Points measurements as well as the results of the analysis of different products;
  • A database connection layer is responsible of interfacing the results of the different processing modules with the database.

The QAT is available to the users through the KEO interface. The Use Case Diagram of the QAT is shown in the following Figure.

Image

 

The QAT has been used within the project to assess the accuracy of geocoded and coregistered products delivered by key service providers.

 

Objectives and Benefits

The main objective of the OrthoServ project is to assess the geometric quality of ortho-rectification and co-registration processes and related products generated by key service providers and based on algorithms developed within the MIR-E and NEST projects. This objective is achieved by performing following key activities:

  1. Definition of a customized Quality Assessment (QA) methodology;
  2. Implementation of a customized Quality Assessment Tool (QAT);
  3. Definition of a Reference Data Set (RDS);
  4. Identification of key service providers and related ortho-rectified and co-registered products;
  5. Assessment of geometric product quality based on the RDS generated by
    • Key service providers and
    • Algorithms developed within the MIR-E and NEST activities.
  6. Assessment of radiometric product quality.
  7. Integration of the QA tools into KEO.

 

Application and Result Expected

The QAT has been implemented and made accessible through the KEO interface. The following main features are available:

  • Assessment of absolute location accuracy of ortho-rectified optical and SAR products based on reference point and linear vector features;
  • Assessment of relative accuracy of coregistered optical and SAR products based on cross-correlation;
  • Analysis of radiometric quality of coregistered optical and SAR products based on rationing;
  • Extraction of candidate control points from the QAT DB;
  • Extraction of candidate linear features from the QAT and the GSHHS and SWBD water bodies databases;
  • Management of the control points and linear features QAT DB.

 

Ortho-rectified and / or coregistered products delivered by 9 service providers and obtained from the following optical and SAR sensors have been assessed:

  • Landsat-7 ETM+
  • SPOT 5 HRG THM
  • Ikonos
  • IRS-P6 LISS-III
  • ALOS PRISM
  • Quickbird
  • Worldview-1
  • ERS-1/2
  • Radarsat-1
  • ENVISAT ASAR
  • ALOS PALSAR
  • TerraSAR-X

 

The analysis of SAR datasets mainly identified two types of approaches in handling these data, one more or fully SAR-specific and one more general. It has been shown that the first approach is capable of delivering superior results, compatible with the accuracies that are expected for the types of sensors and products that have been analysed, while in the other case the full content of the original products is not preserved. 

For the analysis of optical datasets, no co-registered products were delivered. The quality analysis was also made difficult by the fact that many providers did not deliver products. Some delivered insufficient information for the algorithms they use, they used different or own input data and varying areas for the products. The comparison showed differences in the quality of the received products, although they were usually not large, with some notable exceptions. The analysis showed that using GCPs only is not sufficient for quality analysis, as at the most problematic mountainous areas they are scarce. Using visual control, very significant differences and errors in mountainous areas could be detected. Radiometrically, it could be seen that some providers tend to enhance the image contrast, but in general the differences were not big. However, even regarding radiometry, basic errors occur like wrap-around of gray values, saturation and image information loss and step effects, probably due to the gray value interpolation method. This reduced radiometric analysis shows some serious problems, which we suggest that they should be analysed more thoroughly. In case of high-resolution satellites, it could be seen that certain providers seem to have a geometric bias in their delivered products. While no provider delivered really bad products, in some cases the quality was not as good as it could be expected (in theory with good input data the orthoimage planimetric accuracy should be 0.5-1 of the orthoimage pixel size, given that this is close to the original image GSD, i.e. without oversampling). The not-satisfying quality appeared in products, where the DSM and GCPs used, were not the ones provided by the project.


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