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Application of unsupervised nearest-neighbor density-based approaches to sequential dimensionality reduction and clustering of hyperspectral images
2018
Image and Signal Processing for Remote Sensing XXIV
To cite this version: Claude Cariou, Kacem Chehdi. Application of unsupervised nearest-neighbor density-based approaches to sequential dimensionality reduction and clustering of hyperspectral images. ABSTRACT In this communication, we address the problem of unsupervised dimensionality reduction (DR) for hyperspectral images (HSIs), using nearest-neighbor density-based (NN-DB) approaches. Dimensionality reduction is an important tool in the HSI processing chain, aimed at reducing the high
doi:10.1117/12.2325530
fatcat:2tpq5qaya5e3nbh74iezjtoiba