Alleviating Semantic Uncertainty in Cams for Weakly-Supervised Land-Cover Classification

Image-level weakly-supervised land-cover classification (WSLC) relies on image labels to free land-cover classification networks from expensive pixel-level annotations. Existing weakly-supervised methods commonly generate class activation maps (CAMs) from a trained classification network to produce pixel-level pseudo labels for training land-cover classification networks. However, CAMs struggle to completely and accurately capture land-covers in remote sensing images due to the lack of precise localization information in weak supervision. To alleviate semantic uncertainty in CAMs, this paper proposes an Expanding-Separating (ES) scheme to generate high-quality CAMs of land-covers. The ES scheme expands activation areas in CAMs by lifting activation values in inactivated regions and suppresses CAM noises by decreasing values in confusing regions. Experimental results on the DeepGlobe dataset demonstrate that the proposed ES scheme significantly improves the quality of CAMs and benefits the training of land-cover classification, achieving state-of-the-art performance.