摘要
为了解决目前面向多时相遥感影像农作物分割中存在的研究较少、分割精准度较低等问题,设计了一种基于3D特征金字塔网络(FPN)结构的模型,并引入空洞空间卷积池化金字塔(ASPP)模块,使得该网络能够捕获图像的多尺度特征,融合图像更多的上下文语义信息.此外针对数据集类别数量和难易区分程度不平衡的问题,引入了Focal loss函数,通过设置权重因子,使得网络更多关注数量少或难区分的类别.以2019年齐齐哈尔市的多时相遥感影像为数据源,对玉米、大豆、水稻以及其他四种类别进行分割.实验结果显示,总精准度达到了93.6%,平均召回率达到了93.2%,平均准确率达到了94.0%,平均F1得分达到了93.5%,平均交并比(IoU)达到了88.6%,优于3D FCN、3D Unet、2D Unet+CLSTM等农作物分割方法,证明了提出的模型的有效性.
In order to solve the problems of less research,low segmentation accuracy in crop segmentation of multi-temporal remote sensing images at present,this paper designs a model based on 3D Feature Pyramid Net(FPN)structure,and introduces the Atrous Spatial Pyramid Pooling(ASPP)module,making the network capture the multi-scale features of the image and fuse more contextual semantic information of the image.In addition,in order to solve the problem of imbalance in the number of categories and difficulty of distinguishing in the dataset,the Focal loss function is introduced.By setting the weight factor,the network pays more attention to the categories with a small number or indistinguishable.Using the multi-temporal remote sensing images of Qiqihar City in 2019 as the data source,the segmentation of corn,soybean,rice and other four categories was carried out.The experimental results show that overall accuracy reachs 93.6%,average recall reachs 93.2%,average precision reachs 94.0%,average F1 score reachs 93.5%,and average Intersection over Union(IoU)reachs 88.6%,which is better than other crop segmentation methods such as 3D FCN,3D Unet,2D Unet+CLSTM,proving the effectiveness of the proposed model.
作者
贺新乐
高文炜
贺兆
霍鹏程
HE Xinle;GAO Wenwei;HE Zhao;HUO Pengcheng(Xi'an Microelectronics Technology Institute,Xi’an 710054,Shaanxi,China)
出处
《微电子学与计算机》
2023年第8期55-63,共9页
Microelectronics & Computer