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
datasets partitioning. The former limits the generalization performance of the
model and the latter is partitioned leads 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 of 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 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 potentials
in exploring for HSI classification technology. The code can be accessed at
this https URL

我们提出了一种通用的知识嵌入对比学习框架（KnowCL），用于监督、无监督和半监督的高光谱图像分类，该框架基于一套新的高光谱图像处理流程，结合了多种数据转换和增强技术，提供了多样化的数据表示和现实数据分区，同时采用了一种新的损失函数，可以自适应地融合有监督损失和无监督损失，提高了学习性能。