一种改进的多光谱遥感图像超像素分割算法

针对简单线性迭代聚类算法在多光谱遥感图像超像素分割中存在的未充分利用图像特征信息及超像素尺寸、 数量固定导致分割精度较低的问题, 提出将流形 简单线性迭代聚类算法引入到遥感图像超像素分割任务中, 并对其进行改进. 首先, 给出一种基于彩色局部二进制模式改进的多光谱遥感图像纹理特征提取方法； 其次, 扩展流形 简单线性迭代聚类算法的光谱空间, 使算法可以适应高维图像数据； 最后, 改进流形 简单线性迭代聚类算法的聚类距离度量, 融合图像的多段光谱特征、 空间特征及纹理特征对像素进行迭代聚类, 实现内容敏感超像素分割. 实验结果表明, 与现有方法相比, 该算法对多光谱遥感图像的超像素分割结果更准确, 在边缘召回率、 欠分割误差、 可达细分精度指标上均有提升, 能改善多光谱遥感图像分割预处理方法中精度较低的问题.

An Improved Superpixel Segmentation Algorithm of Multi-spectral Remote Sensing Images

Aiming at the problem that the simple linear iterative clustering (SLIC) algorithm in the superpixel segmentation of multi-spectral remote sensing images underutilized the image feature information and the fixed size and number of superpixels leaded to low segmentation accuracy, we proposed to introduce manifold SLIC (MSLIC) algorithm into the task of superpixel segmentation of remote sensing images and improve it. Firstly, we proposed an improved texture feature extraction method for multi-spectral remote sensing images based on color local binary pattern (CoLBP). Secondly, we expanded the spectral space of the MSLIC algorithm so that the algorithm could adapt to high-dimensional image data. Finally, we improved the clustering distance measurement of the MSLIC algorithm, fused the multi-spectral features, spatial features and texture features of the image to perform iterative clustering of pixels to achieve content-sensitive superpixel segmentation. The experimental results show that compared with the existing methods, the proposed algorithm has more accurate superpixel segmentation results of multispectral remote sensing images, and improves the edge recall rate, under segmentation error and subdivision accuracy. It can improve the problems of low accuracy in the preprocessing method of multispectral remote sensing image segmentation.