Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes



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Molina, Iñigo and Martínez, Estíbaliz and Arquero, Águeda and Pajares Martinsanz, Gonzalo and Sánchez, Javier (2012) Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes. Sensors, 12 (3). pp. 3528-3561. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s120303528


Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

Item Type:Article
Uncontrolled Keywords:optical sensors; change detection; thresholding; information fusion; ROC space
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:67844
Deposited On:17 Sep 2021 11:17
Last Modified:17 Sep 2021 11:23

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