论文标题
遥感应用中深度学习模型的半监督微调
Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications
论文作者
论文摘要
提出了两个知名领域的组合方法：深度学习和半监督学习，以解决土地覆盖识别问题。提出的方法证明了对深度学习模型的性能的影响，当SSL方法用作训练过程中的性能功能时。获得的结果是，在正常图像上，在像素级分割任务上，SSL增强的损耗功能在模型的性能中可能是有益的。
A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem. The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training. Obtained results, at pixel level segmentation tasks over orthoimages, suggest that SSL enhanced loss functions can be beneficial in models' performance.
