A New Automatic Selection Method of Optimal Segmentation Scale for High Resolution Remote Sensing Image

The invention discloses a novel automatic selection method of an optimal segmentation scale of a high-resolution remote sensing image. A multi-scale segmentation model of a high-resolution image is established by using a multi-scale MRF model, meanwhile image layer segmentation and image plane modeling are carried out at the same time, and context information between layer and layer objects and the spatial dependency of objects in the same layer are described. Spectrums, colors, textures, topological relations and other basic features of the objects are normalized in a Markov random field, a global optimal segmentation scale selection method capable of being automatically executed by a computer is realized by probabilistic information convergence calculation, parameter selection calculation and inference engineering are automatically executed by the computer, and an optimal segmentation scale parameter on theory is obtained. The technique has the advantages of high segmentation quality precision, high self adaptability and high computational efficiency.