基于改进DeeplabV3+的地物分类方法研究

doi:10.11896/jsjkx.201100184

摘要/Abstract

摘要： 原始DeeplabV3+算法对无人机航拍图像中的地物边缘分割不够准确,对道路的分割存在不连续的情况。因此,针对这些问题,文中对DeeplabV3+算法进行了改进。首先,在编码阶段进行特征融合,增强浅层特征图的语义信息;其次,在分割网络结构中添加边界提取分支模块,并采用Canny边缘检测算法提取真实的边界信息进行监督训练,使网络对地物边缘的分割更为精细;最后,在网络的解码阶段,融合更多的浅层特征。实验结果表明,所提方法的mIoU值为80.92%,比DeeplabV3+算法提升了6.35%,能够有效进行地物分类。

关键词: DeeplabV3+, 边缘检测, 地物分类, 遥感图像, 语义分割

Abstract: The original DeeplabV3+ algorithm is not accurate enough for the edge segmentation of UAV aerial images,and the road segmentation is discontinuous.Therefore,in order to solve these problems,this paper improves the DeeplabV3+ algorithm.Firstly,the feature fusion is carried out in the coding stage to enhance the semantic information of the shallow feature map.Secondly,the boundary extraction branch module is added to the segmentation network structure,and Canny edge detection algorithm is used to extract the real boundary information for supervision training,so that the network can segment the edge of ground objects.Finally,in the decoding stage of the network,more shallow features are fused.The experimental results show that the mIoU value of the proposed method is 80.92%,which is 6.35% higher than that of the DeeplabV3+ algorithm,and can effectively classify the ground objects.

Key words: DeeplabV3+, Edge detection, Land cover classification, Remote sensing image, Semantic segmentation

中图分类号:

TP274

朱戎, 叶宽, 杨博, 谢欢, 赵蕾. 基于改进DeeplabV3+的地物分类方法研究[J]. 计算机科学, 2021, 48(11A): 382-385. https://doi.org/10.11896/jsjkx.201100184

ZHU Rong, YE Kuan, YANG Bo, XIE Huan, ZHAO Lei. Feature Classification Method Based on Improved DeeplabV3+[J]. Computer Science, 2021, 48(11A): 382-385. https://doi.org/10.11896/jsjkx.201100184

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