Patch-Based Cascade Forest Wetland Classification Based on Multi-Temporal SAR Images in Yellow River Delta

Synthetic Aperture Radar (SAR) is vital for coastal wetland mapping due to its cloud and vegetation penetration capabilities. However, using SAR imagery in wetland mapping is challenged by similar backscatter coefficients and speckle noise. To address these issues, a patch-based cascade forest (PBC) method is proposed in this paper, which combines multi-temporal, full-polarization GF-3 SAR data to map typical ground objects in the Yellow River Delta wetlands. Comparative experiments are conducted using different temporal datasets, and the proposed method is compared with Random Forest (RF), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), and multi-Grained Cascade Forest (gc-Forest). The results show that the multi-temporal patch-based cascade forest method, achieving an Overall Accuracy (OA) of 89.46% and a Kappa coefficient of 0.872, significantly outperforms single-temporal methods and other machine learning algorithms, which proved that our method fit for long-term, high-quality wetland classification.