A Weakly Supervised Semantic Segmentation Framework for Medium-Resolution Forest Classification With Noisy Labels and GF-1 WFV Images

Forests are the most widely distributed terrestrial vegetation type and play a significant role in the global carbon cycle and ecological diversity. Accurate and timely forest detection provides essential data for forest management and development. Current forest-related products differ in definition, accuracy, and spatial consistency, making them difficult to use. Therefore, it is necessary to map forest cover under a unified framework. However, detecting forests on a large scale requires high-quality and representative samples, which can be challenging. This study proposes a weakly supervised forest classification framework (WSFCF) that uses noisy labels. The WSFCF is designed to address label generation, correction, and sample location optimization. We employ a spectral–spatial network (SSNet) to extract forest cover accurately for medium-resolution forest classification. The experimental results show that the proposed method outperforms the compared methods, achieving an accuracy of 91.76% OA and 88.28% F1 score on 110 GF-1 WFV images. This supports the subsequent extraction of national-scale forest cover and encourages the mapping of China’s forest cover using GF-1 WFV images. Moreover, the proposed method produces satisfactory outcomes for objects, such as water, farmland, and built-up areas within the study area, demonstrating its effectiveness and potential for transferability.