Applications based on orthocorrected high resolution and hyperspectral images

The availability and variety of high resolution satellite and hyperspectral aerial images have led us to consider how image geometric aspects can condition remote sensing applications . Our main purpose is to underline the importance of images geometric correction; its quality conditions not only positioning, but also object dimensions measurements and shapes which can be very important in specific application as map updating and archaeological investigations, where some shapes could be buried. In this paper two orthoprojection procedure, which have been validated, are presented. Both of them are based on non-parametric self-developed algorithms (Rational Function Model and Neural Network) applied to different types of images aimed to determined their limits and potentialities correlated to their geometric features. Firstly map scale suitability of such data (which depends both on the geometric resolution of images and on the adopted sensor model) has been investigated through planimetric positioning accuracy tests. Presented experiences refer to orthoprojection of a SPOT5 supermode image and to an airborne sensor MIVIS (Multispectral Infrared Visible Imaging Spectrometer) one. Considerations have been then carried out about both geometric and content features of the obtained orthoimages. In particular SPOT5 image has been used for demonstrating how well it can be used for middle scale map updating and how its scale mapping suitability heavily depends on the adopted geometric calibration method. MIVIS image has been used to underline how metric issues are as important as the spectral ones in the particular field of the archaeological investigation. In the first case attention has been, therefore, mainly paid to the geometric content of the SPOT5, while in the second one interpretation problem is also taken into consideration.