雷达学报2016，Vol.5Issue(4)：410-418,9.DOI:10.12000/JR16060
基于多层神经网络的中分辨SAR图像时间序列建筑区域提取
Medium Resolution SAR Image Time-series Built-up Area Extraction Based on Multilayer Neural Network
摘要
Abstract
To improve the accuracy and stability of built-up area extraction from Synthetic Aperture Radar (SAR) image time series, in this paper, we propose a multilayer neural-network-based built-up area extraction method that combines the characters of time-series images. The proposed method coarsely tags single images and obtains a large number of samples from time-series images that have been processed by a histogram specification procedure. To generate a training sample dataset, we use samples generated from one image to determine network depth and select samples with higher accuracy from the sample set taken from the time-series images. The final model is trained by the selected large and high quality training dataset. We perform two comparison experiments with 38 25-m resolution ENVISAT ASAR images. Using the proposed method, we achieved 90.2% minima accuracy and a 0.725 minima Kappa coefficient, which are much higher than those of the three conventional methods. Thus, the accuracy and stability of built-up area extraction are significantly improved. In addition, the method proposed in this paper has the advantages of requiring minimal manual operation, well generalization, and training efficiency.关键词
多层神经网络/合成孔径雷达/时间序列/建筑提取Key words
Multilayer neural network/Synthetic Aperture Radar (SAR)/Time-series/Built-up extraction分类
信息技术与安全科学引用本文复制引用
杜康宁,邓云凯,王宇,李宁..基于多层神经网络的中分辨SAR图像时间序列建筑区域提取[J].雷达学报,2016,5(4):410-418,9.基金项目
国家自然科学基金(61301025)，中国科学院百人计划Foundation Items：The National Natural Science Foundation of China (61301025), Hundred-Talent Program of the Chinese Academy of Sciences （61301025）
