Research on Wetland Fine Classification Based on Remote Sensing Images with Multi-Temporal and Feature Optimization

Wetlands, known as “the kidney of the Earth”, serve as critical ecological carriers for global sustainable development. The fine classification of wetlands is crucial to their utilization and protection. Wetland fine-scale classification based on remote sensing imagery has long been challenged by disturbances such as clouds, fog, and shadows. Simultaneously, the confusion of spectral information among land cover types remains a primary factor affecting classification accuracy. To address these challenges, this paper proposes a fine classification model of wetlands in remote sensing images based on multi-temporal data and feature optimization (CMW-MTFO). The model is divided into three parts: (1) a multi-satellite and multi-temporal remote sensing image fusion module; (2) a feature optimization module; and (3) a feature classification network module. Multi-satellite multi-temporal image fusion compensates for information gaps caused by cloud cover, fog, and shadows, while feature optimization reduces spectral characteristics prone to confusion. Finally, fine classification is completed using the feature classification network based on deep learning. Using coastal wetlands in Liaoning Province, China, as the experimental area, this study compares the CMW-MTFO with several classical wetland classification methods, non-feature-optimized classification, and single-temporal classification. Results show that the proposed model achieves an overall classification accuracy of 98.31% for Liaoning wetlands, with a Kappa coefficient of 0.9795. Compared to the classic random forest method, classification accuracy and Kappa coefficient improved by 11.09% and 0.1286, respectively. Compared to non-feature-based classification, classification accuracy increased by 1.06% and Kappa coefficient by 1.18%. Compared to the best classification performance using single-temporal images, the proposed method achieved a 1.81% increase in classification accuracy and a 2.19% increase in Kappa value, demonstrating the effectiveness of the model approach.