2026-10-11T03:56:02Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34037831 2026-10-01T02:29:25Z category_146 category_272 category_39 category_734 category_931 category_811 category_133 portal_87 item_type_6 month_year_10_2026

An automated mixed-sample generation strategy for land cover classification in heterogeneous river basins Xilin Hu (16427793) Dejun Zhu (20084661) Danxun Li (23739786) Space Science Environmental Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Plant Biology Land cover training sample generation random forest multi-source satellite data <p>Land cover (LC) classification in heterogeneous areas is challenging because pixels are often influenced by multiple LC types and show less distinctive feature responses. 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. In cross-product non-full-agreement areas, mIoU further increased by 0.083 and 0.129. The results suggest that the strategy provides a simple automated option for regional LC mapping.</p> 2026-10-01T02:29:25Z Text Journal contribution 10.6084/m9.figshare.34037831.v1 https://figshare.com/articles/journal_contribution/An_automated_mixed-sample_generation_strategy_for_land_cover_classification_in_heterogeneous_river_basins/34037831 CC BY 4.0