摘要
针对遥感图像自然地貌边缘的像素点归类问题,提出融合边缘检测模块的多通道融合模型与解码器端模块模型。边缘检测模块以Canny算子为基础进行闭运算及均值滤波处理得到精确化的图像边缘。语义分割网络以DeepLabV3+为基础,分别从编码器及解码器端并联边缘计策模块。实验结果表明,改进后的2种网络相比原DeepLabV3+网络在高分辨率自然地貌图像数据集上均取得更好的分割效果,且解码器端融合网络取得了最高72.60%的交互比(IoU,intersection over union)和86.64%的F1score,可用于面向自然地貌的识别与分割。
To classify pixels of natural landform edges in remote sensing images, this paper proposes a multi-channel fusion model and a decoder-side module model both integrating an edge detection module.The edge detection module takes the Canny operator as the base to perform closed operations and mean filtering, as a result of which accurate image edges can be achieved. Based on DeepLabV3+, the semantic segmentation network is connected with an edge planning module in parallel at encoder and decoder sides respectively. The experimental results show that the two improved networks can achieve a better segmentation effect on a high-resolution natural landform image data set compared with the original DeepLabV3+ network. Particularly, the network with fusion at the decoder side achieves the highest intersection over union(IoU) of 72.60% and F1 score of 86.64%, which can be used for the recognition and segmentation of natural landforms.
作者
沈祺宗
高春艳
Shen Qizong;Gao Chunyan(School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2022年第2期293-302,共10页
Journal of System Simulation
基金
国家自然科学基金重点项目(U1913211)。
