摘要
为了提升多光谱图像语义分割的精度,提出了一种基于局部二值模式(LBP)特征增强的语义分割神经网络模型。该模型通过两个大小分别为3×3和5×5的LBP特征提取算子,对原始红外图像进行边缘信息提取,获得了边缘特征图。将原始RGB图像、红外图像和获得的LBP特征图输入到一个包含34层残差网络的模型中进行语义分割。实验结果表明,本文提出的基于LBP特征增强的神经网络模型,在RGB-Thermal数据集上取得了60.7%的平均准确率和51.9%的平均交并比,明显优于其他对比模型。同时在可视化结果上,本文模型的结果也更加清晰准确。
In order to improve the accuracy of multi-spectral image semantic segmentation,a neural network model based on local binary pattern(LBP)feature enhancement is proposed.The model obtains two feature maps from a single infrared image by two LBP feature extraction operators with the size of 3×3 and 5×5,respectively.The RGB image,the infrared image,and the LBP feature maps are imported into a neural network model with a 34-layer residual network for semantic segmentation.The experimental results show that the proposed neural network model can achieve an average accuracy of 60.7%and an average intersection over union of 51.9%on the RGB-Thermal dataset.The results are superior to other comparative methods.At the same time,in the visualization results,the results of proposed model are also more clear and accurate.
作者
史兴萍
徐江涛
蒋永唐
秦书臻
路凯歌
Shi Xingping;Xu Jiangtao;Jiang Yongtang;Qin Shuzhen;Lu Kaige(School of Microelectronics,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology,Tianjin 300072,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第14期46-53,共8页
Laser & Optoelectronics Progress
基金
天津市应用基础与前沿技术研究计划(17ZXRGGX00040)。
关键词
图像处理
多光谱语义分割
局部二值模式
残差神经网络
image processing
multi-spectral semantic segmentation
local binary pattern
residual neural network论文信息
