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[期刊论文]

Pseudolabel-Based Unreliable Sample Learning for Semi-Supervised Hyperspectral Image Classification

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author:

Yao, Huaxiong (Yao, Huaxiong.) [1] | Chen, Renyi (Chen, Renyi.) [2] | Chen, Wenjing (Chen, Wenjing.) [3] | Unfold

Indexed by:

EI Scopus SCIE

Abstract:

Recently, pseudolabel-based deep learning methods have shown excellent performance in semi-supervised hyperspectral image (HSI) classification. These methods usually select high-confidence unlabeled samples to help optimize backbone classification networks. However, a large number of remaining low-confidence unlabeled samples, which contain rich land-covers information, are underused. In this article, we propose a pseudolabel-based unreliable sample learning (PUSL) method to fully exploit low-confidence unlabeled samples for semi-supervised HSI classification. First, to avoid overfitting the spatial distribution of labeled samples, we build a position-free transformer (PFT) as the backbone classification network. Second, PFT is initially trained with labeled samples in a supervised learning manner to obtain an initial classifier, which is then used to split unlabeled samples into reliable and unreliable unlabeled samples based on the predicted confidence. Third, reliable unlabeled samples participate in training along with labeled samples. Finally, unreliable unlabeled samples are treated as negative samples for the corresponding categories to improve the discrimination of PFT in a contrastive learning paradigm. Extensive experiments on three HSI datasets demonstrate that PUSL outperforms the compared methods.

Keyword:

Contrastive learning hyperspectral image (HSI) classification pseudolabel semi-supervised learning

Community:

  • [ 1 ] [Yao, Huaxiong]Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China
  • [ 2 ] [Chen, Renyi]Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China
  • [ 3 ] [Sun, Hao]Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China
  • [ 4 ] [Xie, Wei]Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China
  • [ 5 ] [Yao, Huaxiong]Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China
  • [ 6 ] [Chen, Renyi]Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China
  • [ 7 ] [Sun, Hao]Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China
  • [ 8 ] [Xie, Wei]Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China
  • [ 9 ] [Chen, Wenjing]Hubei Univ Technol, Sch Comp Sci, Wuhan 430068, Peoples R China
  • [ 10 ] [Lu, Xiaoqiang]Fuzhou Univ, Coll Phys & Informat Engn, Fuzhou 350002, Peoples R China

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Source :

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN: 0196-2892

Year: 2023

Volume: 61

7 . 5

JCR@2023

7 . 5 0 0

JCR@2023

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 22

SCOPUS Cited Count: 31

30 Days PV: 7

Online/Total:24/10094180
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