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Communication Dans Un Congrès Année : 2019

Constrained Distance Based K-Means Clustering for Satellite Image Time-Series

Résumé

The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available k-Means constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is thought to be more appropriate for time-series analysis. Adding constraints to the clustering problem increases accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions.
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Dates et versions

hal-02356722 , version 1 (08-11-2019)
hal-02356722 , version 2 (12-12-2019)

Identifiants

Citer

Thomas Lampert, Baptiste Lafabregue, Pierre Gançarski. Constrained Distance Based K-Means Clustering for Satellite Image Time-Series. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, Jul 2019, Yokohama, Japan. pp.2419-2422, ⟨10.1109/IGARSS.2019.8900147⟩. ⟨hal-02356722v2⟩
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