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Proxy-Based Deep Learning Framework for SpectralSpatial Hyperspectral Image Classification: Efficient and Robust
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DOI：10.1109/TGRS.2021.3054008.png)
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Abstract
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Deep convolutional networks have been extensively deployed in hyperspectral image (HSI) classification. Reaching for high accuracy, the existing deep-learning-based methods commonly deepen or widen their networks for better performance, which brings higher computational complexity and the risk of overfitting. Although the introduction of the residual module and batch-normalization reduces the generalization degradation in complex networks, the mainstream methods still suffer from low robustness to the noise. To tackle these issues, a compact proxy-based deep learning framework is proposed to perform highly accurate HSI classification with superb efficiency and robustness. In this article: 1) novel deep proxies are integrated to replace the dense classifier layers in conventional networks, which represents specific classes in deep embedding space and enables fast and reliable convergence; 2) the proxy-based feature embedding is studied in distance metric and similarity metric, and compatible dual-metric loss functions are designed for further optimized embedding distribution, which leads to more robust generalization; and 3) state-of-the-art performance and robustness are demonstrated by the proposed framework on mainstream HSI data sets with the minimal network scale and time complexity.
Keywords:
Deep learning
Feature extraction
Robustness
Measurement
Hyperspectral imaging
Training data
Training
3-D deep learning
convolutional neural networks (CNNs)
hyperspectral image (HSI)
HSI classification
proxy-based learning
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