2016
DOI: 10.3390/s16101621
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Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

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 sensitiv… Show more

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Cited by 7 publications
(5 citation statements)
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References 58 publications
(87 reference statements)
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“…Changes in the radiometric response or texture observed may indicate LULC changes [21,52]. Because changes in image response can arise from sources other than LULC change, a variety of ways to enhance the analysis have been developed [53][54][55][56]. Specific approaches dedicated to digital surface model comparison also exist.…”
Section: State Of the Artmentioning
confidence: 99%
“…Changes in the radiometric response or texture observed may indicate LULC changes [21,52]. Because changes in image response can arise from sources other than LULC change, a variety of ways to enhance the analysis have been developed [53][54][55][56]. Specific approaches dedicated to digital surface model comparison also exist.…”
Section: State Of the Artmentioning
confidence: 99%
“…However, image radiometry comparison at pixel level often leads to noisy results. Therefore, other raw image comparison methods use texture information instead, e.g., by computing mutual information between images at two epochs over wider windows (Gueguen, Soille, and Pesaresi 2011;Molina et al 2016). DSM comparison is a specific case among these raw data comparison approaches: it is easier to use in operational situations due to the physical meaning of height differences, and then to define realistic and stable thresholds or to derive change probabilities (Chaabouni-Chouayakh et al 2010;Guerin, Binet, and Pierrot-Deseilligny 2014;Champion et al 2010).…”
Section: Remote Sensing Change Detectionmentioning
confidence: 99%
“…As a result of the high performance of the multi-sensor data fusion method on the noise elimination to the process control application [25], it was chosen in order to analyze the control process of a bulk tobacco curing schedule in this study. The multi-sensor data fusion method has been widely used in various research areas [17,26,27,28,29,30,31]. In the field of artificial sensors’ applications, which are highly related to the present study, a feature level fusion with principal component analysis (PCA) feature selection method and several pattern analysis techniques, such as ANN, linear discriminant analysis (LDA), partial least square (PLS), and support vector machine (SVM), have been mostly used for food authentication and the on-line monitoring of food fermentation processes [30,32,33,34].…”
Section: Introductionmentioning
confidence: 99%