Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET

2025-09-17

AI Summary

Existing deep learning models (e.g., 1D-Justo-LiuNet) suffer significant performance degradation in few-shot hyperspectral spectral classification due to their reliance on large-scale labeled data. Method: This paper proposes a parameter-free, highly data-efficient end-to-end classification framework. Its core innovation lies in MiniROCKET and HDC-MiniROCKET—two non-parametric feature extractors leveraging random convolutional kernels and hash-based encoding—to transform 1D spectral sequences into highly discriminative representations, directly fed into a lightweight classifier. Contribution/Results: By eliminating learnable parameters, the method avoids overfitting and exhibits superior robustness and generalization under extreme data scarcity. Experiments across multiple hyperspectral benchmarks demonstrate that it achieves comparable overall accuracy to 1D-Justo-LiuNet using far fewer training samples—and substantially outperforms it in ultra-low-shot regimes (e.g., ≤5 samples per class). These results validate its practical utility in real-world few-shot applications such as precision agriculture, medical diagnostics, and remote sensing.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Representation Learning for Vision
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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

Problem

• Improving spectral classification with limited training data
• Mitigating performance deterioration in data-efficient scenarios
• Enhancing hyperspectral pixel classification using MiniROCKET variants

Innovation

• MiniROCKET for spectral classification
• HDC-MiniROCKET with engineered features
• Parameter-free feature extraction method