自动化学报2017，Vol.43Issue(2)：215-226,12.DOI:10.16383/j.aas.2017.c150660
基于深度学习和层次语义模型的极化SAR分类
Polarimetric SAR Image Classification Based on Deep Learning and Hierarchical Semantic Model
摘要
Abstract
Stacked auto-encoder model can effectively represent the complex terrain structures,such as the urban and the forest,by automatically learning high-level features.However,it has difficulty in preserving details and edges.In order to overcome this shortcoming,a new unsupervised polarimetric synthetic aperture radar (PolSAR) classification method is proposed by combining the deep learning and the polarimetric hierarchical semantic model (PHSM).According to the PHSM,a PolSAR image is partitioned into aggregated,homogeneous and structural regions.For aggregated regions,a stacked auto-encoder model is applied to learn high-level features,and further the sparse representation and classificatiou is constructed by learning a dictionary with high-level features.For homogeneous regions,hierarchical segmentation and classification is applied.In addition,edges are located and line objects are preserved for structural regions.Experimental results demonstrate that the proposed method can obtain good performance in both region homogeneity and edge preservation.关键词
叠自编码器/极化层次语义模型/极化SAR分类/区域划分/层次分割Key words
Stacked auto-encoder/polarimetric hierarchical semantic model (PHSM)/polarimetric synthetic aperture radar (SAR) image classification/region partition/hierarchical segmentation引用本文复制引用
石俊飞,刘芳,林耀海,刘璐..基于深度学习和层次语义模型的极化SAR分类[J].自动化学报,2017,43(2):215-226,12.基金项目
国家重点基础研究发展计划(973计划)(2013CB329402),国家自然科学基金(61573267,61571342,61572383),国家自然科学基金青年科学基金项目(31300473),教育部“长江学者和创新团队发展计划”(IRT1170),高等学校学科创新引智计划(B07048),福建省自然科学基金(2014J01073)资助 Supported by National Basic Research Program of China(973 Program) (2013CB329402),Natural Science Foundation of China (61573267,61571342,61572383),Youth Fund of National Natural Science Foundation of China (31300473),the Program for Cheung Kong Scholars and Innovative Research Team in University (IRT1170),the Fund for Foreign Scholars in University Research and Teaching Programs (B07048),Natural Science Foundation of Fujian Province (2014J01073) （973计划）
