Geometric correction and validation of Hyperion and ALI data for EVEOSD

Abstract

Precise geometric correction of EO-1's Hyperion data is essential to link ground spectral data and satellite hyperspectral data. Two scenes have been selected from sites of the EVEOSD (Evaluation and Validation of EO-1 for Sustainable Development of Forests) project. One site is the Greater Victoria Watershed District (GVWD) located on south Vancouver Island, BC and the other is Hoquiam located in southwestern Washington State. Ground Control Point (GCP) collection has been performed using a feature fitting method in which high accuracy, orthorectified photo-derived polygons of features are used for tie-down. For example lakes are adjusted to match the same feature obvious in the hyperspectral imagery. This technique allows for easier estimation of a GCP's precise fit to the imagery. A third (11) of the GCPs were identified as check points to validate the geometric models. GCPs were collected independently from both the VNIR and SWIR arrays of the Hyperion sensor to determine the adjustment factor required to remove the displacement and skew between these arrays. The adjustment can then be applied to GCPs collected from one array to make a compatible geometric correction model for both arrays. The polynomial and rational function correction methods have been applied to both scenes with various orders applied to each function. The effect of terrain distortion removal is evaluated in using the rational function method. Hyperion data can be geocorrected with surprising accuracy. For example, we obtained 10 m RMS on check points with the rational function. With a second order polynomial we achieved 13 m RMS without terrain correction. The accuracy of this latter result is due to the small swath width of the sensor. Applying terrain correction does improve the accuracy of geometric correction in areas with high relief. A similar procedure was applied to EO-1's ALI sensor and this paper compares the results for Hyperion and ALI geometric fidelity.