The recent focus on automation in remote sensing image analysis has raised attention to robust learning with enormous amounts of data with noisy labels. Nevertheless, research on deep learning with noisy datasets has been rarely designed for semantic segmentation on remote sensing data. To address this issue, we present a case study on a mangrove satellite image dataset with noisy labels and aim to improve pixel-wise classification accuracy and regional coherence. Our method combines data selection and edge-enhancement techniques in noisy data learning. The results demonstrate that the proposed method outperforms the previous data selection method and suggest that this combination is effective in mitigating the negative effects of noisy labels.