A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification
This work addresses the issue of inflated evaluation metrics and poor real-world performance in hyperspectral image classification, which is important for researchers and practitioners in remote sensing and computer vision, though it appears incremental as it builds on existing contrastive learning and data augmentation techniques.
The authors tackled the problem of hyperspectral image classification by proposing a universal knowledge embedded contrastive learning framework (KnowCL) that closes the gap between pocket models and standard vision backbones, achieving improved generalization and realistic data partitioning.
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 \url{https://github.com/quanweiliu/KnowCL}.