An automated mixed-sample generation strategy for land cover classification in heterogeneous river basins

This study proposes an automated mixed-sample generation strategy for improving LC classification in heterogeneous river basins. The strategy combines cross-product full-agreement pixels and partial-agreement pixels from multiple LC products to balance label reliability and sample diversity.

Full-agreement pixels provide reliable labels, while partial-agreement pixels add information from low-agreement areas that may include heterogeneous and transitional LC conditions. The method was tested in two Chinese river basins using random forest classification. Compared with a baseline using only full-agreement pixels, mean Intersection over Union (mIoU) increased from 0.762 to 0.809 in one basin and from 0.729 to 0.782 in the other.