融入超像素分割的高分辨率影像面向对象分类

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。

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

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.