Name
Title | ||
|---|---|---|
Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach - A Part of the TIMELINE Preprocessor. |
Abstract | ||
|---|---|---|
The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series analysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore, a fusion of two techniques to match the data in orbit projection has been developed to overcome this limitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to find the best match between small image chips taken from a reference water mask in the first, and from a median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is determined shifting around the small image chips until the best correlation between reference and satellite data source is found for each respective image part. Only if both attempts result in the same shift in any direction, the position in the orbit is included in a third order polynomial warping process that will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the latitude and longitude layers and the contents of the data layers itself remain untouched. As such, original sensor measurements are preserved. An included automated quality assessment generates a quality layer that informs about the reliability of the matching. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/rs9040303 | REMOTE SENSING |
Keywords | Field | DocType |
AVHRR,chip matching,geolocation,georeferencing,TIMELINE | Computer vision,Satellite,Image warping,Geolocation,Remote sensing,Geographic coordinate system,Advanced very-high-resolution radiometer,Preprocessor,Pixel,Artificial intelligence,Geology,Compositing | Journal |
Volume | Issue | ISSN |
9 | 4 | 2072-4292 |
Citations | PageRank | References |
2 | 0.58 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas J. Dietz | 1 | 23 | 9.04 |
| c m frey | 2 | 14 | 3.40 |
| Thomas Ruppert | 3 | 2 | 0.92 |
| martin bachmann | 4 | 10 | 3.92 |
| Claudia Kuenzer | 5 | 142 | 28.94 |
| S. Dech | 6 | 171 | 28.32 |