Unsupervised Subpixelic Classification Using Coarse-Resolution Time Series and Structural Information

Abstract : In this paper, a new method is presented for a subpixelic land cover classification using both high-resolution structural information and coarse-resolution (CR) temporal information. To that aim, the linear mixture model is used for pixel disaggregation. It enables us to describe a CR time series in terms of the mixture of classes that are represented within each pixel. Then, the Bayes' rule and the maximum a posteriori criterion lead to the definition of an energy function whose minimum corresponds to the researched optimal classification. A theoretical analysis of the labeling errors that may be obtained using this energy function is provided, raising the main parameters for labeling performance. The optimal classification is computed by combining linear regressions and simulated annealing, leading to an unsupervised algorithm. The method is validated with numerical results obtained on two different agricultural scenes (i.e., the Danubian plain and the Coet Dan watershed).
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IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2008, 46 (5), pp.1359 - 1374. <10.1109/TGRS.2008.916477>
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https://hal.archives-ouvertes.fr/hal-00624504
Contributeur : Lionel Moisan <>
Soumis le : lundi 19 septembre 2011 - 01:59:23
Dernière modification le : mercredi 4 janvier 2017 - 16:23:17

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Amandine Robin, Sylvie Le Hégarat-Mascle, Lionel Moisan. Unsupervised Subpixelic Classification Using Coarse-Resolution Time Series and Structural Information. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2008, 46 (5), pp.1359 - 1374. <10.1109/TGRS.2008.916477>. <hal-00624504>

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