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Iterative Spatial-Spectral Training Sample Augmentation for Effective Hyperspectral Image Classification
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DOI：10.1109/LGRS.2021.3131373.png)
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摘要
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Factors such as insufficient training samples, high-dimensional data features, and unbalanced data classes can degrade the accuracy of hyperspectral classification. To this end, this letter proposes an iterative training sample augmentation (ITSA) algorithm and a new classification model incorporating ITSA and maximum margin projection (ITSA-MMP). First, ITSA iteratively augments samples by a similar region clustering strategy (SRCS) integrating spatial-spectral metric. Then, box-plot for representative sample selection (BPRSS) is adopted to screen optimal samples for the final augmented sample set (ASS). Next, based on the ASS, MMP projects the hyperspectral image into a low-dimensional subspace to explore the local structure of the data manifold and improve the interclass separability of the data. Finally, the MMP-reduced data is classified by support vector machines. Experiments on two real hyperspectral datasets validate that ITSA-MMP can effectively increase the training sample set especially for small initial sample set and unbalanced dataset and obtain a higher classification accuracy.
Keyword:
Training
Support vector machines
Manifolds
Hyperspectral imaging
Redundancy
Classification algorithms
Principal component analysis
Box-plot (BP)
hyperspectral image classification (HSIC)
iterative training sample augmentation (ITSA)
maximum margin projection (MMP)
similar region clustering strategy (SRCS)
期刊
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16.4
论文数:
1.0W
被引数:
5.1K
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