Change detection techniques are widely diffused to derive basic information in the analysis of land cover transformations. Some difficulties in multi-date imagery treatment persist in remote sensing applications because of errors due to noise, to environmental conditions and to geometric and radiometric distortions introduced during the acquisition or transmission phases of satellite systems. Such assumptions may have impact on the accuracy of subsequent human or machine-assisted multi-temporal image analysis. Preliminary geometric and radiometric processing of remotely sensed data are consequently necessary in order to correct degradations and noises introduced during the imaging process. The methods of radiometric correction for multi-temporal analysis of satellite imagery can be absolute and relative. The absolute methods are not always feasible because they need to measure the optical properties of the atmosphere acquired “in situ” and simultaneously with the moment of scene recording. The relative methods proceed under the assumption that the relationship between the at-sensor radiances recorded at two different times from regions of constant reflectance is spatially homogeneous and can be approximated by linear functions. The most difficult time-consuming aspect of all these methods is the determination of suitable time invariant features upon which the normalisation is based (PIF - Pseudo Invariant Features) with the scarce possibility to homogenise data in accurate way. The research proposes the investigation of normalization methods in order to prepare next Change detection techniques for land cover transformations, executable on satellite data that are heterogeneous for spatial and spectral resolution. To that end, the most suitable radiometric correction techniques and the development of innovative algorithms and automatic methodology were conducted 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. Finally, an empirical test by analysing the Gain and Offset results was proposed in order to allow the selection of bands with optimal behaviour within MAD transformation, usable for next multitemporal analysis (classifications, vegetation index analysis, etc.).
|Titolo:||Comparison of Radiometric Normalization Methods on LANDSAT ETM+ and ASTER Data|
|Data di pubblicazione:||2008|
|Appare nelle tipologie:||1.1 Articolo in rivista|