基于改进SLIC与区域邻接图的高分辨率遥感影像建筑物提取

Building Extraction of High Resolution Remote Sensing Image Based on Improved SLIC and Region Adjacency Graph

针对传统SLIC超像素算法在高分辨率遥感影像上分割质量差的问题,提出一种基于降维的改进SLIC与区域合并的方法对建筑物进行分割.首先,对传统SLIC的五维计算进行降维简化,采用灰度特征信息替换色彩信息,减少LAB颜色空间五维特征向量表征的冗余;其次,采用区域邻接图对过分割图像进行合并;最后,对改进SLIC中的主要参数即超像素数目k、紧凑度m和迭代次数p对分割结果的影响做了分析与比较.实验表明:该方法不仅分割出了大部分的建筑物信息,还提高了算法的运行效率与空间效率.运行时间效率比传统SLIC提高了25.5%;对建筑物的提取精度能达到97.6%.

Aiming at the problem that the traditional SLIC algorithm has poor quality in segmenting high resolution remote sensing images, this paper proposes an improved SLIC based on dimensionality reduction and region merging to segment the buildings. Firstly,it simplifies the dimensionality of the traditional SLIC, and the color information is replaced by the gray feature information to reduce the redundancy of the five-dimensional feature vector of the LAB color space. Secondly, the over-segmentation images are combined by using the region adjacency graph. Finally, the main parameters of the improved SLIC are analyzed and compared, namely, the number of super-pixels ＇k＇, the compactness ＇m＇ and the number of iterations ＇p＇. The experiments show that this method can not only separate most of the building information, but also improve the operation efficiency and space efficiency. The running time efficiency is 25.5% higher than the traditional SLIC, and the segmentation precision of the building can achieve 97.6%.