LAUSR

In recent years, the global commercial aerospace industry has flourished, witnessing a rapid surge in customized satellite services. Deep learning has emerged as a pivotal tool for accurately identifying wetland vegetation. However, hyperspectral remote sensing images are often plagued by varying degrees of noise during acquisition, leading to subtle differences in spectral responses. Currently, vegetation classification models are tailored specifically for each hyperspectral sensor, making it challenging to generalize a model designed for one sensor to others. Furthermore, discrepancies in data distribution between training and test sets result in a notable decline in model performance, impeding model sharing across satellite hyperspectral sensors and hindering the interpretation of wetland scenes. Domain adaptation methods leveraging Generative Adversarial Networks (GANs) have been extensively researched and applied in the realm of cross-sensor land feature classification. Nevertheless, these data-level cross-domain classification strategies typically focus on band selection or alignment using relatively similar data to address image differences, without addressing spectral variability or incorporating pseudo-labels to enhance classification accuracy. Noise changes aggravate the distribution characteristics and model differences of vegetation in classification tasks. This has a negative impact on subsequent classification accuracy. To alleviate these problems, we have designed a linear unbiased stochastic network classification framework based on adversarial learning. The framework employs a style randomization algorithm to simulate spectral drift. It generates simulated images to enhance the model’s generalization ability. Supervised contrastive learning is utilized to prevent redundant learning of the same training images. Domain discrimination and domain-invariant characteristics are considered. We optimize the generator and discriminator using inter-class and intra-class contrast loss functions. The dual regularization training method is adopted, and non-redundant expansion is realized. It achieves similarity and addresses offsets. This method minimizes computational cost. Cross-sensor classification experiments were conducted, with comparative tests performed on a self-made wetland dataset. This method demonstrates significant advantages in wetland vegetation classification. According to the visualization results, our classification strategy can be used for cross-domain vegetation classification in coastal wetlands. It can also be applied to other small-satellite hyperspectral images and cross-satellite multispectral data, reducing on-site sampling costs and proving cost-effective.