摘要
针对传统特征表征能力较弱的问题,提出了一种基于多层深度特征融合的极化合成孔径雷达图像语义分割方法;利用经过预训练的VGG-Net-16模型提取表征能力更强的多层图像特征,再将各层深度特征分别用于训练对应的条件随机场模型,最后将多个条件随机场模型的输出结果进行融合,实现了最终的图像语义分割。结果表明:与基于传统经典特征的方法相比,所提方法取得了最高的总体分类精度,说明所提方法采用的融合特征具有比传统特征更强的表征能力。
Aiming at the problem that the traditional feature representation ability is weak, we propose a polarization synthetic aperture radar image semantic segmentation method based on the multi-layer deep feature fusion. The pre-trained VGG-Net-16 model is used to extract multi-layer image features with strong representation ability, and then deep features of each layer are used to train the corresponding conditional random field model. The output results of multiple conditional random field models are finally merged to realize the final semantic segmentation of the images. The results show that compared with the methods based on classical features, the proposed method achieves the highest overall accuracy, indicating that the fusion features used by the proposed method have stronger representation ability than traditional features.
作者
胡涛
李卫华
秦先祥
Hu Tao;Li Weihua;Qin Xianxiang(Institute of Information and Navigation,Air Force Engineering University,Xi′an,Shaanxi 710077,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2019年第2期244-250,共7页
Chinese Journal of Lasers
基金
国家自然科学基金(41601436
61403414
61703423)
陕西省自然科学基础研究计划(2018JM4029
2016JQ6070)
关键词
图像处理
多层深度特征融合
语义分割
条件随机场
卷积神经网络
image processing
multi-layer deep feature fusion
semantic segmentation
conditional random field
convolutional neural network
