Orthoprojection Tests of Hyperspectral Data in Steep Slope Zones

Nowadays hyperspectral data are really important in the environmental field. While the advantages, due to their radiometric features are broadly documented a rigorous metric verification is still absent especially in uneven areas (mountains) where the presence of steep slopes produces strong deformations on the images that have therefore to be preventively corrected. The classification potentialities and the high number of bands of the hyperspectral data is already known by operators. Nevertheless these risk to rest unusable if a good correction of the scenes geometry is not guaranteed. The mountain zones represent a critical benchmark for both warping and orthoprojection algorithms; therefore they have been chosen during this study. Positioning accuracy (planimetric) tests have been conducted on airborne sensor MIVIS images (Multispectral Infrared Visible Imaging Spectrometer). Such system is based on the whiskbroom digital acquisition technology. The rigorous definition of the projective model still remains an open problem subordinated to the external orientation auxiliary data. However the sensor model problem cannot be neglected because of the strong geometric deformations of the images that can make them useless or improper for the mapping scales suggested by their average geometric resolution. This study shows some orthoprojection results obtained both by commercial software and by autonomous procedures (developed by the authors) based on self-calibrating Rational Function Model and on Multi Layer Perceptron neural network. A comparison among the different methodologies has been conducted taking care of the geometric accuracy in order to define the most appropriate map scale they could be addressed to. A MIVIS image has been select for the test, recorded with an across-valley flight on the middle Valley of Susa (Turin-Italy), where the elevation range is about 1800 meters.