Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network

Autor:	Mateus Habermann, Vincent Frémont, Elcio Hideiti Shiguemori
Jazyk:	English<br />French
Rok vydání:	2018
Předmět:	Instruments and machines QA71-90 Applied optics. Photonics TA1501-1820 Cellular telephone services industry. Wireless telephone industry HE9713-9715
Zdroj:	Revue Française de Photogrammétrie et de Télédétection, Iss 217-218 (2018)
Druh dokumentu:	article
ISSN:	1768-9791 2426-3974
DOI:	10.52638/rfpt.2018.419
Popis:	Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality. Thus, band selection is a practice that has been adopted before any further processing takes place. Therefore, in this paper, a new unsupervised method for band selection based on clustering and neural network is proposed. A comparison with six other band selection frameworks shows the strength of the proposed method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/b31efe8f93e54ae4b516303eb73a2489 Zobrazit plný text záznamu View record in DOAJ