基于多层次场聚类的高分辨率遥感影像分割方法

Field Theory Based Multi-level Clustering for High-Spatial Resolution Remote Sensing Imagery Segmentation

提出一种基于多层次场聚类的高分辨率遥感影像分割方法。首先,在梯度影像上进行标记分水岭变换,得到初始分割结果;然后,依据光谱、纹理和空间关系等特征,构建影像对象聚类场。最后,建立场约束规则,实现对象合并,并通过多层次聚类得到最终分割结果。实验表明,该方法能有效解决初始分割中的过分割问题,在一定程度上提高分割精度。

A multi-level field clustering approach for segmentation of high spatial resolution imagery is proposed in this paper. Initial image segmentation is firstly achieved by marker-based watershed transform based on gradient image. And then the clus- tering fields consist of the image objects which are built according to spectral, texture and location relationship. Finally, image objects are merged by means of the clustering with the consideration of filed rules, and multi-level results are obtained through multi-level clustering. The experiments demonstrate that the proposed method can effectively solve the over-segmentation prob- lem of segmentation, and to a certain extent,improve the segmentation accuracy.