基于自监督学习的河流分割方法

Segmentation of River Based on Self-supervised Learning

针对水上桥梁图像受地形、天气、环境的影响,河流情况复杂且形式较多,无法事先采集所有图像中的河流样本等问题,本文提出一种基于自监督学习的河流分割方法,利用K均值聚类与Harris角点相结合的方法自动提取部分河流区域作为自监督学习的河流样本,以及河流样本的颜色和纹理特征,再根据提取的图像的河流特征利用支持向量机(SVM)的单分类功能进行训练学习,通过训练好的分类器完成河流的分割。实验结果表明,本文的河流分割方法能较好地分割出河流并适应不同场景的水上桥梁图像。

For the problems such as the complexity of the river because of the bridge images being caused by terrain,weather and environment,and hardly collecting all river samples of the images,a segmentation of river based on self-supervised learning is proposed. The approach uses the part of the river area automatically extracted by combining the K-means clustering method with Harris corner method as river sample in self-supervised learning,according to the color and texture feature extracted from river sample,trains the sample with the one class support vector machine. Then the river is segmented by the trained classifier. The experimental results demonstrate the proposed method has good performance in automatically segmenting river and can adapt to bridge images in different scenarios.