Semi-supervised PolSAR Image Classification Based on the Neighborhood Minimum Spanning Tree

Leida xuebao

XIE Wen¹  HUA Wenqiang²  WANG Shuang³  GUO Yanhe³ 

[1] ①(School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China);①(School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China)②(Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi’an 710121, China);③(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, 710071, China);

关键词: PolSAR;    Terrain classification;    Semi-supervised learning;    Minimum spanning tree;   

DOI  :  10.12000/JR18104

来源: DOAJ

【 摘 要 】

In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree (NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.

【 授权许可】

Unknown