基于数据融合的半监督高分遥感影像语义分割

Semi-supervised Semantic Segmentation for High-resolution Remote Sensing Images Based on Data Fusion

由于需要进行像素级标注,语义分割通常比分类以及目标识别等任务需要更高的人工成本,尤其在基于高分遥感影像的土地分类应用中,因其背景复杂、目标密集,进行语义标注的成本更为高昂,严重限制了该技术在智能遥感领域的发展。此外,尽管传统半/弱监督学习方法能够有效降低训练成本,但通常其分割结果的质量较低,很难具备应用价值。针对以上两个问题,文中提出了一种采用半监督自校正融合策略的语义分割模型。通过引入数据融合技术以及自校正策略,有效地降低了分割模型对强标注的依赖性。该模型在仅使用15%强监督信息的前提下,在波茨坦以及韦兴根数据集上分别获得了86.5%和81.7%的平均F1分数。实验结果表明,所提方法在大幅降低语义分割训练成本的同时,能够获得与全监督模型相竞争的高质量分割结果。

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.