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A new k-nearest neighbor density-based clustering method and its application to hyperspectral images

Abstract : In this communication, we propose a new unsupervised clustering method, which uses a kNN graph to propagate labels, starting from high density regions of the representation space. A feature of this method is the fact that it only requires setting the number of neighbors of each object, a problem which can be addressed easily thanks to the clustering stability of the proposed approach. A multiresolution setting is also proposed to allow clustering image pixels. Preliminary results obtained on real hyperspectral images show the efficiency of the proposed clustering method with respect to classical approaches often used in remote sensing. © 2016 IEEE.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01484548
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, March 7, 2017 - 1:22:31 PM
Last modification on : Tuesday, October 6, 2020 - 3:10:07 AM

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Claude Cariou, K. Chehdi. A new k-nearest neighbor density-based clustering method and its application to hyperspectral images. 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Jul 2016, Beijing, China. pp.6161--6164, ⟨10.1109/IGARSS.2016.7730609⟩. ⟨hal-01484548⟩

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