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Parameter-Free Density Estimation for Hyperspectral Image Clustering

Abstract : This paper investigates multivariate kernel density estimation for hyperspectral data. More specifically, it focuses on designing a data-driven, parameter-free and fast estimator that is able to deal with clusters of different size, shape and density. We first propose a new criterion to compare density estimates, based on how evenly they treat the different classes in the data. In addition, we propose a simple, parameter-free and non-iterative kernel bandwidth selection approach. We show that it is substantially more adaptive than existing estimators on six remote sensing hyperspectral datasets. © 2018 IEEE.
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Submitted on : Wednesday, April 3, 2019 - 2:42:16 PM
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S.L. Moan, Claude Cariou. Parameter-Free Density Estimation for Hyperspectral Image Clustering. 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018, Nov 2018, Auckland, New Zealand. pp.8634706, ⟨10.1109/IVCNZ.2018.8634706⟩. ⟨hal-02089118⟩



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