A Novel Semi-supervised Long-tailed Learning Framework with Spatial Neighborhood Information for Hyperspectral Image Classification

Deep learning technologies have been successfully applied to hyperspectral(HS) image classification with remarkable performance. However, compared with traditional machine learning methods, neural networks usually need more data. In remote sensing(RS) research, obtaining a large number of labeled HS...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2023-01, Vol.20, p.1-1
Main Authors: Feng, Yining, Song, Ruoxi, Ni, Weihan, Zhu, Junheng, Wang, Xianghai
Format: Article
Language:English
Subjects:
Classification
Convolutional neural networks
Deep learning
Feature extraction
Hyperspectral Image
Hyperspectral imaging
Image classification
Imbalanced Sample Classification
Long-tailed Distributions
Machine learning
Methods
Neighborhoods
Neural networks
Remote sensing
Semi-supervised Learning
Semisupervised learning
Tail
Task analysis
Training
Unbalance
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Summary:Deep learning technologies have been successfully applied to hyperspectral(HS) image classification with remarkable performance. However, compared with traditional machine learning methods, neural networks usually need more data. In remote sensing(RS) research, obtaining a large number of labeled HS data is a very difficult and expensive work. Simultaneously, the distribution of feature information is bound to be unbalance, and tends to conform to the long tail. At present, the neighborhood information of unlabeled samples is usually ignored in HS image classification tasks based on semi-supervised learning. In this letter, we propose a new semi-supervised long tail learning framework based on spatial neighborhood information(SLN-SNI), which can complete the HS image classification task under unbalanced small sample data. Specifically, a new semi-supervised learning strategy is proposed. On this basis, a new method to determine the label of unlabeled samples based on spatial neighborhood information(SNI) is proposed. The coarse classification results divided into three situations are judged again, and the accuracy of pseudo labels is improved. The performance of the proposed method is tested on three public HS image datasets. Compared with the current advanced methods have achieved a certain improvement.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3241340