Hyperspectral unmixing using deep convolutional autoencoder

Hyperspectral Unmixing (HU) estimates the combination of endmembers and their corresponding fractional abundances in each of the mixed pixels in the hyperspectral remote sensing image. In this paper, we address the linear unmixing problem with an unsupervised Deep Convolutional Autoencoder network (...

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Bibliographic Details

Published in:	International journal of remote sensing Vol. 41; no. 12; pp. 4799 - 4819
Main Authors:	Elkholy, Menna M., Mostafa, Marwa, Ebied, Hala M., Tolba, Mohamed F.
Format:	Journal Article
Language:	English
Published:	London Taylor & Francis 17.06.2020 Taylor & Francis Ltd
Subjects:	Coders data collection Datasets extracts Feature extraction image analysis Mean square errors Noise reduction Remote sensing Root-mean-square errors
ISSN:	0143-1161, 1366-5901, 1366-5901
Online Access:	Get full text
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