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Sensors 2016, 16(10), 1621; doi:10.3390/s16101621

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

1
ETSITGC, Technical University of Madrid, 28031 Madrid, Spain
2
ETSIInf, Technical University of Madrid, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Received: 22 July 2016 / Revised: 16 September 2016 / Accepted: 22 September 2016 / Published: 30 September 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
View Full-Text   |   Download PDF [7657 KB, uploaded 30 September 2016]   |  

Abstract

In 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
Keywords: change detection; radiometric normalization; thresholding; informational metrics; sensor fusion; Support Vector Machine; quality assessment change detection; radiometric normalization; thresholding; informational metrics; sensor fusion; Support Vector Machine; quality assessment
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MDPI and ACS Style

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.

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