Few-Shot Hyperspectral Image Classification Based on Adaptive Subspaces and Feature Transformation

In the field of hyperspectral image (HSI) classification, deep learning has helped achieve great successes. However, most of these achievements are made with very large amounts of labeled training data. Manual annotation of HSIs is labor intensive and time consuming. In practical HSI classification,...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-17
Main Authors: Bai, Jing, Huang, Shaojie, Xiao, Zhu, Li, Xianmin, Zhu, Yongdong, Regan, Amelia C., Jiao, Licheng
Format: Article
Language:English
Subjects:
3-D local channel attention residual network (3D-ECA-ResNet)
Adaptation models
adaptive subspace classifier
Annotations
Classification
Datasets
Deep learning
Feature extraction
featurewise transformation
few-shot learning
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Labour
Learning systems
Machine learning
metalearning
Prototypes
Subspaces
Task analysis
Training
Transformations (mathematics)
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Summary:In the field of hyperspectral image (HSI) classification, deep learning has helped achieve great successes. However, most of these achievements are made with very large amounts of labeled training data. Manual annotation of HSIs is labor intensive and time consuming. In practical HSI classification, there may only be a few labeled samples available. To perform HSI classification with a small number of labeled samples, a new few-shot classification model based on adaptive subspaces and featurewise transformation is proposed in this article. First, we design a 3-D local channel attention residual network to obtain the spatial-spectral features of HSIs. Then, a featurewise transformation strategy is introduced to enhance feature diversity to avoid model overfitting problems and to mitigate the impact of cross-domain problems. Finally, a subspace classifier is implemented to construct different subspace categories based on the embedded features of the limited labeled samples. Classification of an HSI sample is performed using spatial projection and a distance metric. The proposed model is trained using the metalearning mechanism to perform few-shot classification of HSIs. Four public datasets are utilized to construct a sufficient few-shot classification task named episodes for training. The other three public datasets are used to test the proposed model. Experiments show that our proposed method can outperform mainstream small sample HSI classification methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3149947