融入超像素分割的高分辨率影像面向对象分类  

Object-oriented classification of high-resolution image combining super-pixel segmentation

作　　者：聂倩 七珂珂 赵艳福 NIE Qian;QI Keke;ZHAO Yanfu(Ningbo Institute of Surveying and Mapping and Remote Sensing Technology,Ningbo 315000,China;Ningbo Alatu Digital Science and Technology Co.,Ltd.,Ningbo 315000,China)

机构地区：[1]宁波市测绘和遥感技术研究院,浙江宁波315000 [2]宁波市阿拉图数字科技有限公司,浙江宁波315000

出　　处：《测绘通报》2021年第6期44-49,共6页Bulletin of Surveying and Mapping

摘　　要：针对高分辨率遥感影像面向对象分类中容易受分割参数的影响、分类精度不稳定的问题,本文提出了一种融入超像素分割的高分辨率影像面向对象分类方法。该方法通过简单线性迭代聚类(SLIC)算法对原始影像进行聚类生成超像素影像,并在此基础上采用分形网络演化方法(FNEA)进行多尺度分割生成同质性对象,最后利用最邻近分类方法进行地物分类。试验结果表明,该方法不易受多尺度分割参数的影响,分类效果稳定,而且分类精度明显高于传统的面向对象分类方法,对于高分辨率遥感影像的广泛应用具有重要意义。In order to solve the problem that high-resolution remote sensing image object-oriented classification is easy to be affected by segmentation parameters and the classification accuracy is not stable,this paper proposes an object-oriented classification of high-resolution image combining super-pixel segmentation.In this method,a simple linear iterative clustering algorithm is used to cluster the original image to generate the super-pixel image.On this basis,the fractal net evolution approach is used for multi-scale segmentation to generate homogeneous objects.Finally,the nearest neighbor classification method is used to classify the ground objects.The experimental results show that the method is not easily affected by multi-scale segmentation parameters,the classification effect is stable,and the classification accuracy is significantly higher than that of the traditional object-oriented classification method,which is of great significance for the wide application of high-resolution remote sensing images.

关 键 词：高分辨率遥感影像 简单线性迭代聚类 超像素 分形网络演化方法 多尺度分割 面向对象分类 

分 类 号：P237[天文地球—摄影测量与遥感]