An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyl...

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Bibliographic Details
Main Authors:	JongCheol Pyo, Hongtao Duan, Mayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Yong Sung Kwon, Hyuk Lee, Taegu Kang, Kyunghyun Kim, YoonKyung Cha, Kyung Hwa Cho
Format:	Article
Language:	English
Published:	MDPI AG 2020-03-01
Series:	Remote Sensing
Subjects:	deep learning stacked autoencoder cyanobacteria hyperspectral image feature extraction dimensionality reduction
Online Access:	https://www.mdpi.com/2072-4292/12/7/1073

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