The quality of information derived from processed remotely sensed data may depend upon many factors, mostly related to the extent data acquisition is influenced by atmospheric conditions, topographic effects, sun angle and so on. The goal of radiometric corrections is to reduce such effects in order enhance the performance of change detection analysis. There are two approaches to radiometric correction: absolute and relative calibrations. Due to the large amount of free data products available, absolute radiometric calibration techniques may be time consuming and financially expensive because of the necessary inputs for absolute calibration models (often these data are not available and can be difficult to obtain). The relative approach to radiometric correction, known as relative radiometric normalization, is preferred with some research topics because no in situ ancillary data, at the time of satellite overpasses, are required. In this study we evaluated three well known relative radiometric correction techniques using two Landsat 8 - OLI scenes over a subset area of the Apulia Region (southern Italy): the IR-MAD (Iteratively Reweighted Multivariate Alteration Detection), the HM (Histogram Matching) and the DOS (Dark Object Subtraction). IR-MAD results were statistically assessed within a territory with an extremely heterogeneous landscape and all computations performed in a Matlab environment. The panchromatic and thermal bands were excluded from the comparisons.