Superpixel Segmentation Based on Anisotropic Diffusion Model for Object-Oriented Remote Sensing Image Classification

Superpixel segmentation is an essential step of object-oriented remote sensing image classification; the accuracy of the superpixel segmentation boundary will directly affect the classification result. Most of the traditional superpixel segmentation algorithms rely on spectral similarity and spatial connectivity to construct superpixels. They cannot find the accurate boundary in the complex scenes, such as the spatial distribution of ground features being relatively broken, and large differences in the size and shape, especially long-thin shape and circular shape. Aiming at this problem, a superpixel segmentation algorithm based on an anisotropic diffusion model named ADS is proposed and applied to image classification. The anisotropic diffusion model originated in thermodynamics has excellent properties in which the diffusion is continuous and smooth and its diffusion speed depends on the medium, which provides convenience for smoothing homogeneous regions and establishing boundary constraints for different ground objects. With this advantage, the diffusion flux model is established to consider the influence of boundary factors and used to simulate the dissimilarity measure with boundary constraints between pixels and seed points by combining the traditional spectral and spatial distance. Then, the seed points of superpixel are optimized under the K-means framework. The effectiveness of the proposed algorithm is tested and verified with different spatial resolutions, such as Landsat 8 with 30 m, Sentinel-2 with 10 m, and SkySat with 0.5 m. A large number of experiments show that the proposed algorithm can better correct the superpixel boundary-fitting deviation problem in complex scenes and effectively promote the improvement of image classification accuracy.