A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification

arXiv:2404.01673 · cs.CV · Submitted 2024-04-02 · Read on arXiv

cs.CV

Submitted: 2024-04-02

Updated: 2024-04-27

Journal ref: Q. Liu, Y. Dong, T. Huang, L. Zhang, and B. Du, "KnowCL: A Universal Knowledge Embedded Contrastive Learning framework for hyperspectral image classification," Knowledge-Based Systems, vol.352, p.116972, Oct. 2026

DOI: 10.1016/j.knosys.2026.116972

Code: https://github.com/quanweiliu/KnowCL

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed.

Terminology

Abstract

Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. However, these HSI classification models are confined to pocket models and unrealistic ways of dataset partitioning. The former limits the generalization performance of the model and the latter is partitioned leading to inflated model evaluation metrics, which results in plummeting model performance in the real world. Therefore, we propose a universal knowledge embedded contrastive learning framework (KnowCL) for supervised, unsupervised, and semisupervised HSI classification, which largely closes the gap between HSI classification models between pocket models and standard vision backbones. We present a new HSI processing pipeline in conjunction with a range of data transformation and augmentation techniques that provide diverse data representations and realistic data partitioning. The proposed framework based on this pipeline is compatible with all kinds of backbones and can fully exploit labeled and unlabeled samples with the expected training time. Furthermore, we design a new loss function, which can adaptively fuse the supervised loss and unsupervised loss, enhancing the learning performance. This proposed new classification paradigm shows great potential in exploring for HSI classification technology. The code can be accessed at.

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