南京林业大学学报（自然科学版）2024，Vol.48Issue(4)：93-103,11.DOI:10.12302/j.issn.1000-2006.202209055
基于注意力机制和改进DeepLabV3+的无人机林区图像地物分割方法
UAV forestry land-cover image segmentation method based on attention mechanism and improved DeepLabV3+
摘要
Abstract
[Objective]This study proposes the feature segmentation method Tree-DeepLab for unmanned aerial vehicle(UAV)forest images,based on an attention mechanism and the DeepLabV3+semantic segmentation network,to extract the main feature distribution information in forest areas.[Method]First,the forest images were annotated according to feature types from six categories(Platanus orientalis,Ginkgo biloba,Populus sp.,grassland,road,and bare ground)to obtain the semantic segmentation datasets.Second,the following improvements were made to the semantic segmentation network:(1)the Xception network,the backbone of the DeepLabV3+semantic segmentation network,was replaced by ResNeSt101 with a split attention mechanism;(2)the atrous convolutions of different dilation rates in the atrous spatial pyramid pooling were connected using a combination of serial and parallel forms,while the combination of the atrous convolution dilation rates was simultaneously changed;(3)a shallow feature fusion branch was added to the decoder;(4)spatial attention modules were added to the decoder;and(5)efficient channel attention modules were added to the decoder.[Result]Training and testing were performed based on an in-house dataset.The experimental results revealed that the Tree-DeepLab semantic segmentation model had mean pixel accuracy(mPA)and mean intersection over union(mIoU)values of 97.04%and85.01%,respectively,exceeding those of the original DeepLabV3+by 4.03 and 14.07 percentage points,respectively,and outperforming U-Net and PSPNet.[Conclusion]The study demonstrates that the Tree-DeepLab semantic segmentation model can effectively segment UAV aerial photography images of forest areas to obtain the distribution information of the main feature types in forest areas.关键词
无人机/地物分割/林区图像/DeepLabV3+/注意力机制/ResNeStKey words
unmanned aerial vehicle(UAV)/land-cover image segmentation/forestry images/DeepLabV3+/attention mechanism/ResNeSt分类
农业科技引用本文复制引用
赵玉刚,刘文萍,周焱,陈日强,宗世祥,骆有庆..基于注意力机制和改进DeepLabV3+的无人机林区图像地物分割方法[J].南京林业大学学报（自然科学版）,2024,48(4):93-103,11.基金项目
国家林业和草原局重大应急科技项目(ZD202001) （ZD202001）
国家重点研发计划(2021YFD1400900). （2021YFD1400900）
