A Band Selection Method With Masked Convolutional Autoencoder for Hyperspectral Image-Reference-Cited by-同舟云学术

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A Band Selection Method With Masked Convolutional Autoencoder for Hyperspectral Image

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Liu Yufei¹^ORCID,Li Xiaorun¹^ORCID,Hua Ziqiang¹^ORCID,Xia Chaoqun²^ORCID,Zhao Liaoying³^ORCID

Affiliation:

1. Department of Electrical Engineering, Zhejiang University, Hangzhou, China

2. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China

3. Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China

Funder

National Nature Science Foundation of China

Joint Fund Project of Chinese Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09784433.pdf?arnumber=9784433

Reference13 articles.

1. Kernel-OPBS Algorithm: A Nonlinear Feature Selection Method for Hyperspectral Imagery

2. BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image

3. Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm

4. Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering

5. A Geometry-Based Band Selection Approach for Hyperspectral Image Analysis

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