Radiometric Calibration Methods for Change Detection Analysis of Satellite Data Aimed at Environmental Risk Monitoring

The research proposes the investigation of automatic methods, in order to prepare next Change detection techniques for environmental risk monitoring, executable on satellite data that are heterogeneous for spatial and spectral resolution. Homogenization and registration in an unique digital information environment, with the identification and quantification of variation occurred in a chosen test area, will permit the rapid evaluation of risk level and the consequent planning of prevention and intervention works. To that end, the most suitable radiometric correction techniques and the development of innovative algorithms and automatic methodology were executed, in order to improve the accuracy level of results. With this aim, the relative radiometric normalization scene-to-scene with ELC (Empirical Line Calibration) and MAD (Multivariate Alteration Detection) techniques on Landsat ETM+ and ASTER data were investigated. In the ELC technique Pseudo-Invariant Features (PIFs) were manually selected, whereas the Features to derive the normalization coefficients were automatically identified with the aid of an algorithm based on MAD transformation. The exactness of both the procedures was evaluated by executing a quantitative and qualitative comparison of gains and offset values resulted in the analysis.