Iterative Semi-Supervised Learning With Few-Shot Samples for Coastal Wetland Land Cover Classification

A novel approach is proposed in this study that combines superpixel (SP) segmentation and multiclassifier ensemble learning (EL) to address the limited availability of labeled samples in coastal wetland land cover classification. First, the SP segmentation technique is employed to partition unknown samples into multiple homogeneous regions, thereby facilitating the effective capture of spatial information pertaining to land cover. Subsequently, a multiclassifier EL strategy is employed within these regions to process the samples, effectively leading to a reduction in classification errors and an improvement in accuracy. To enhance the performance of semi-supervised learning (SSL), a sample iteration selection metric is introduced to optimize the training samples based on the consistency of sample types within homogeneous regions and the results obtained from the multiclassifier ensemble, thus enhancing the reliability of pseudo-labels. Additionally, multiscale SP segmentation is utilized to augment the ensemble strategy for samples in order to reduce the necessity for hyperparameter adjustments and increase the automation and reliability of the model. Overall, the accuracy of coastal wetland classification is improved by this approach while simultaneously mitigating the complexity of SSL in terms of hyperparameter tuning. The effectiveness of the proposed approach has been assessed through experiments conducted on three GF-5 hyperspectral images of coastal wetlands in China. In particular, the proposed methods provide superior performance compared with the state-of-the-art classification methods.