Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes
AbstractIn this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal “invariant features” is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a “change map”, which can be accomplished by means of the CDI’s informational content. For this purpose, information metrics such as the Shannon Entropy and “Specific Information” have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf’s) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances. View Full-Text
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Molina, I.; Martinez, E.; Morillo, C.; Velasco, J.; Jara, A. Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes. Sensors 2016, 16, 1621.
Molina I, Martinez E, Morillo C, Velasco J, Jara A. Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes. Sensors. 2016; 16(10):1621.Chicago/Turabian Style
Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro. 2016. "Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes." Sensors 16, no. 10: 1621.
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