地理空间信息2026，Vol.24Issue(4)：126-129,4.DOI:10.3969/j.issn.1672-4623.2026.04.026
交叉验证的半监督遥感图像分割方法
Semi-supervised Remote Sensing Image Segmentation Method Based on Cross-validation
摘要
Abstract
Most of the remote sensing image processing methods rely on supervised training,and the model accuracy depends on the labeled data.Based on the deep learning encoding-decoding semantic segmentation architecture,we proposed a semi-supervised remote sensing image semantic segmentation method with two-model cross-validation.Firstly,we used a small number of labeled images to iterate the model parameters for supervised training.And then,we let the unlabeled data pass through the two models to get the pseudo-labels.Finally,we used the pseudo-labels to perform cross-supervised training.Experimental result shows that the method uses the unlabeled data to train deep learning model,effectively reducing the training cost.关键词
遥感图像/语义分割/半监督/交叉验证Key words
remote sensing image/semantic segmentation/semi-supervised/cross-validation分类
天文与地球科学引用本文复制引用
李昊燔,黎娟,孔令寅..交叉验证的半监督遥感图像分割方法[J].地理空间信息,2026,24(4):126-129,4.基金项目
全国教育科学规划重点课题资助项目(DJA200310) （DJA200310）
陕西省教育科学规划课题(SGH23Y3003) （SGH23Y3003）
西安航空职业技术学院校级课题(23XHZK-24) （23XHZK-24）
西安航空职业技术学院2021年度科技创新团队资助项目(KJTD21-001). （KJTD21-001）
