Dual-Channel Residual Network for Hyperspectral Image Classification With Noisy Labels

Hyperspectral image (HSI) classification has drawn increasing attention recently. However, it suffers from noisy labels that may occur during field surveys due to a lack of prior information or human mistakes. To address this issue, this article proposes a novel dual-channel residual network (DCRN)...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-11
Main Authors: Xu, Yimin, Li, Zhaokui, Li, Wei, Du, Qian, Liu, Cuiwei, Fang, Zhuoqun, Zhai, Lin
Format: Article
Language:English
Subjects:
Classification
Deep learning
Feature extraction
Hyperspectral image (HSI)
Hyperspectral imaging
Image classification
Information processing
Labels
Noise measurement
Noise robustness
noise-robust deep learning
noisy labels
Robustness
Surveys
Task analysis
Training
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Summary:Hyperspectral image (HSI) classification has drawn increasing attention recently. However, it suffers from noisy labels that may occur during field surveys due to a lack of prior information or human mistakes. To address this issue, this article proposes a novel dual-channel residual network (DCRN) to resolve HSI classification with noisy labels. Currently, the influence of noisy labels is reduced by simply detecting and removing those anomalous samples. Different from such a specifically designed noise cleansing method, DCRN is easy to implement but highly effective. It enhances its model robustness to noisy labels to a great extent by employing a novel dual-channel structure and a noise-robust loss function. In this way, DCRN can mitigate influence from noisy labels while fully utilizing useful information from mislabeled samples for augmented training. Experiments are conducted on several hyperspectral data sets with manually generated noisy labels to demonstrate its excellent performance. The code is available at https://github.com/Li-ZK/DCRN-2021 .
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3057689