With the development of deep learning, hyperspectral image classification (HSIC) has improved rapidly in recent years. Unsupervised feature learning algorithms play an important role in extracting features from hyperspectral images (HSIs). This letter proposed a random-occlusion-based Bootstrap-Your-Own-Latent network (ROBYOL), combining a new augmentation method and a superior contrastive learning algorithm for feature extraction. The proposed method consists of a self-supervised learning part for feature extracting and a classifier part as the downstream task. It can be proved by the experimental results that the feature extraction ability of the network is effective in this way. Furthermore, the influence of different occlusion strategies is also studied, including changing occlusion area and occlusion value, and we proposed translucent occlusion. Our results with two well-known HSIs reveal that proper occlusion strategies can improve hyperspectral classification results effectively.

Despite the rapid development of deep learning in hyperspectral image classification (HSIC), most models require a large amount of labeled data, which are both time-consuming and laborious to obtain. However, contrastive learning can extract spatial–spectral features from samples without labels, which helps to solve the above problem. Our focus is on optimizing the contrastive learning process and improving feature extraction from all samples. In this study, we propose the Unlocking-the-Potential-of-Data-Augmentation (UPDA) strategy, which involves adding superior data augmentation methods to enhance the representation of features extracted by contrastive learning. Specifically, we introduce three augmentation methods—band erasure, gradient mask, and random occlusion—to the Bootstrap-Your-Own-Latent (BYOL) structure. Our experimental results demonstrate that our method can effectively improve feature representation and thus improve classification accuracy. Additionally, we conduct ablation experiments to explore the effectiveness of different data augmentation methods.

Enhancing classification accuracy in hyperspectral image classification under small sample scenarios remains a critical challenge. This study introduces a novel framework based on Conditional Denoising Diffusion Implicit Models (DDIM), featuring an adaptive synthetic sample generation strategy and label noise training. Our approach dynamically allocates synthetic samples for each class based on classification difficulty and generation reliability, while label noise training enhances the model's generalization capabilities. As a flexible data augmentation method, our framework can be seamlessly integrated with various feature extraction techniques, demonstrating significant potential for further performance improvements. Validated on the Salinas and Pavia University datasets, our method highlights the effectiveness of diffusion models as a robust tool for addressing data scarcity, paving the way for broader applications in hyperspectral image classification and remote sensing.

Self-supervised learning effectively leverages the information from unlabeled data to extract spatial-spectral features that are both representative and discriminative, partially addressing the challenge of high data annotation costs in hyperspectral image classification. Inspired by the success of redundancy reduction-based self-supervised learning in other domains, we introduce it into HSIC. We proposed a spatial-spectral feature extraction network, HyperBT, to more effectively reduce redundancy. Specifically, we added the off-diagonal terms of the cross-covariance matrix to the loss function and new data augmentation methods, including band bisection and edge weakening. Experimental results demonstrate that our method achieves high accuracy in classification, surpassing many state-of-the-art methods. Through ablation experiments, we validate the effectiveness of each component in the loss function.

Self-supervised learning has made significant strides in hyperspectral image classification. For contrastive learning, traditional data augmentation methods will lead to biases with real-world spectra, limited variability, loss of information. To address those limitations, this study introduces diffusion models to generate realistic synthetic samples as views input to contrastive learning. This diffusion-based augmentation enables our contrastive learning network to learn more robust spectral-spatial representations. Experimental results on two publicly available datasets indicate that this approach outperforms numerous existing methods. And the ablation study demonstrates the effectiveness of employing diffusion models.