Abstract: 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.

Key words: high-resolution remote sensing image, simple linear iterative clustering, super-pixel, fractal net evolution approach, multi-scale segmentation, object-oriented classification