A New Threshold Relative Radiometric Correction Algorithm (TRRCA) of Multiband Satellite Data
It is well known that remote sensed scenes could be affected by many factors and, for optimum change detection, these unwanted effects must be removed. In this study a new algorithm is proposed for PIF (Pseudo Invariant Features) extraction and relative radiometric normalization. The new algorithm can be labeled as a supervised one and combines three methods for the detection of PIFs: Moment distance index (MDI), Normalized Difference Vegetation Index (NDVI) masks morphological erosion and dilate operators. In order to prove its effectiveness, the algorithm was tested by using Landsat 8 scenes of the “Mar de Plstico” landscape of the Andalusian Almería. Many tests were performed in order to provide a set of valid input parameters for the chosen environments. Lastly, the results were statistically assessed with parametric and non-parametric tests showing very good and stable results in the four different study areas.
KeywordsRelative radiometric normalization PIF Multispectral imagery Landsat 8 Change detection
This work was supported by the Spanish Ministry of Economy and Competitiveness (Spain) and the European Union FEDER funds (Grant Reference AGL2014-56017-R). It takes part of the general research lines promoted by the Agrifood Campus of International Excellence ceiA3.
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