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Multilabel Sample Augmentation-Based Hyperspectral Image Classification
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DOI：10.1109/TGRS.2019.2962014.png)
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摘要
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The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor-intensive and time-consuming. In this article, a multilabel training sample augmentation method is proposed. Instead of giving an exact label to each pixel, we just precisely label a small number of pixels by giving them a single label (called single-label samples) and annotate a large number of pixels in certain regions together by giving them multiple labels (called multilabel samples). Furthermore, in order to make full use of the multilabel training samples, a superpixel segmentation and recursive filtering-based method is proposed. The proposed method consists of the following major steps: recursive filtering-based feature extraction, superpixel-based segmentation, and spectral & x2013;spatial similarity-based mislabeled sample removal. Experimental results demonstrate that the proposed method can significantly improve the classification accuracy of multiple classifiers by using the multilabel training samples.
Keyword:
Classifier
hyperspectral image classification
multilabel samples
sample augmentation
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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引用论文
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