The use of suitable pseudo-invariant targets for MIVIS data calibration by the empirical line method

Abstract

The Empirical Line Method (ELM) enables the calibration of multi- and hyper-airborne/satellite image converting DN or radiance to reflectance values performed by using at ground data. High quality outcome can be reached with the selection of appropriate Pseudo-Invariant Targets (PIT). In this paper, spectral variability of “usual” (asphalt and concrete) and “unusual” (calcareous gravel, basaltic paving, concrete bricks, tartan paving and artificial turf) PITs is retrieved for ELM application. Such PITs are used to calibrate the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne sensor in 12 different Runs. Firstly, processing of field spectral data enables the evaluation of pseudo-invariance of targets by studying their spectral changes in space and in time. Finally, these surfaces are used as Ground Calibration (GCT) and Validation Targets (GVT) in ELM. High calibration accuracy values are observed in Visible (VIS) range (98.9%) while a general decrease of accuracy in Near-InfraRed (NIR) (96.6%) and Middle-InfraRed (SWIR) (88.1%) are reached. Outcomes show that “usual” surfaces as asphalt and concrete and “unusual” surfaces such as tartan can be successfully used for MIVIS image calibration.