交叉验证的半监督遥感图像分割方法OA

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.