用无参特征提取提升小样本高光谱分类性能
Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
- 采用无训练参数的MiniROCKET提取光谱特征
- 小样本下准确率超越1D-Justo-LiuNet，通用场景持平
- 适合数据稀缺的遥感、农业等应用
高光谱图像的像素光谱分类广泛应用于农业、医疗及遥感等领域，正拓展至自动驾驶。尽管全图分类通常结合空间与光谱信息，仅基于光谱信息的方法具有模型更小、训练数据需求少等优势，且可为后续空间-光谱联合方法提供补充。近期提出的1D-Justo-LiuNet以极少参数实现当前最优性能，但在训练数据有限时表现下降。为此，本文研究MiniROCKET与HDC-MiniROCKET在光谱分类中的应用。该方法在特征提取阶段无需训练参数，对小样本更具鲁棒性。实验表明，尽管MiniROCKET参数更多，其在小样本场景下优于1D-Justo-LiuNet，通用情况下基本持平。
原文摘要 · Abstract (English)
The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such as autonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral information has its own advantages, e.g. smaller model size and thus less data required for training. Moreover, spectral information is complementary to spatial information and improvements on either part can be used to improve spatial-spectral approaches in the future. Recently, 1D-Justo-LiuNet was proposed as a particularly efficient model with very few parameters, which currently defines the state of the art in spectral classification. However, we show that with limited training data the model performance deteriorates. Therefore, we investigate MiniROCKET and HDC-MiniROCKET for spectral classification to mitigate that problem. The model extracts well-engineered features without trainable parameters in the feature extraction part and is therefore less vulnerable to limited training data. We show that even though MiniROCKET has more parameters it outperforms 1D-Justo-LiuNet in limited data scenarios and is mostly on par with it in the general case
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