Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/15016
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dc.contributor.authorDeepa C.
dc.contributor.authorShetty A.
dc.contributor.authorNarasimhadhan A.V.
dc.date.accessioned2021-05-05T10:16:12Z-
dc.date.available2021-05-05T10:16:12Z-
dc.date.issued2020
dc.identifier.citationInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , Vol. 43 , B3 , p. 389 - 394en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-archives-XLIII-B3-2020-389-2020
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15016-
dc.description.abstractDimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis. The rapid advances in hyperspectral remote sensing has brought in a lot of opportunities to researchers to come up with advanced algorithms to analyse such voluminous data to better explore earth surface features. Modern machine learning algorithms can be applied to explore the underlying structure of high dimensional hyperspectral data and reduce the redundant information through feature extraction techniques. Limited studies have been carried out on dimensionality reduction for mineral exploration. The current study mainly focuses on the application of autoencoders for dimensionality reduction and provides a qualitative (visual) analysis of the obtained representations. The performance of autoencoders are investigated on Cuprite scene. Coranking matrix is used as evaluation criteria. From the obtained results it is evident that, deep autoencoders provide better results compared to single layer autoencoders. An increase in the number of hidden layers provides a better embedding. The neighborhood size K ≥ 40 of deep autoencoders provides a better transformation compared to autoencoders which shows an improved embedding only after K ≥ 80. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.en_US
dc.titleQuality assessment of dimensionality reduction techniques on hyperspectral data: A neural network based approachen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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