Multiple Level Sets Segmentation for High Resolution Remote Sensing Imagery

The paper presents an unsupervised segmentation model based on multiple level sets evolution for high resolution satellite imagery.The model can remove ambiguities occurring to multiple levels sets methods.In the numerical implementation,a novel re-initialization technique for level set functions is suggested to accelerate curves evolution steadily.Experiments show that our method can alleviate "pepper and salt effect" compared with pixel-based method,and to lessen over-and under-segmentation compared to object-based methods.