Representativeness and Redundancy-Based Band Selection for Hyperspectral Image Classification`-Reference-Cited by-同舟云学术

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Representativeness and Redundancy-Based Band Selection for Hyperspectral Image Classification`

Published:2021-02-11 Issue:9 Volume:42 Page:3534-3562
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Liu Yufei¹,Li Xiaorun²,Feng Yueming³,Zhao Liaoying⁴,Zhang Wenqiang¹

Affiliation:

1. Electric Engineering Department, Zhejiang University, Hangzhou, Zhejiang, China

2. Electrical Engineering Department, Zhejiang University, Hangzhou, Zhejiang, China

3. Chief Engineer Office, State Grid Jiaxing Power Supply Company, Jiaxing, Zhejiang, China

4. Department of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.1875511

Reference51 articles.

1. Hyperspectral Data Processing

2. Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery

3. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification

4. Constrained band selection for hyperspectral imagery

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images;Remote Sensing;2023-11-25

2. An Unsupervised Band Selection Method Based on Triple Constraints and Attention Network for Hyperspectral Imagery;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

3. Segmentation-based linear discriminant analysis with information theoretic feature selection for hyperspectral image classification;International Journal of Remote Sensing;2023-06-03

4. Crop-specific hyperspectral band selection method using limited ground-truth data;International Journal of Remote Sensing;2022-12-17

5. Sparsity Regularized Deep Subspace Clustering for Multicriterion-Based Hyperspectral Band Selection;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022

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