Abstract: Due to the need for pixel-wise annotation,semantic segmentation usually requires higher labor costs than tasks such as classification and object recognition.Especially in land classification based on high-resolution remote sensing images,complex backgrounds and dense targets make semantic annotation intolerably expensive,which seriously limits the practicability of semantic segmentation algorithms.In addition,although traditional semi/weak supervised learning methods can effectively reduce trai-ning costs,it is difficult to have high application value for the low quality of the segmentation results.In order to solve the above two pain points,this paper proposes a new semi-supervised semantic segmentation model using a self-correcting fusion strategy.By introducing data fusion technology and self-correction mechanism,the dependence of the segmentation model on pixel-wise annotation can be effectively reduced.Our method obtains mean F1-scores of 86.5% and 81.7% on Potsdam and Vaihingen datasets with only 15% pixel-wise annotation.Experimental results show that the proposed model can greatly reduce the cost of training process,and achieve high-quality segmentation results comparable to fully-supervised prediction.

Key words: Remote sensing image, Deep learning, Fully convolutional network, Semantic segmentation, Data fusion, Semi-supervised learning