地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (5): 949-961.doi: 10.12082/dqxxkx.2022.210597
陈振1,2,3(), 陈芸芝1,2,3,*(
), 吴婷1,2,3, 李佳优1,2,3
收稿日期:
2021-09-30
修回日期:
2021-12-03
出版日期:
2022-05-25
发布日期:
2022-07-25
通讯作者:
* 陈芸芝(1982— ),女,福建福州人,博士,副研究员,主要研究方向为环境遥感和林业遥感等。 E-mail: chenyunzhi@fzu.edu.cn作者简介:
陈 振(1996— ),男,福建南平人,硕士生,主要研究方向为遥感影像信息提取。E-mail: n195527003@fzu.edu.cn
基金资助:
CHEN Zhen1,2,3(), CHEN Yunzhi1,2,3,*(
), WU Ting1,2,3, LI Jiayou1,2,3
Received:
2021-09-30
Revised:
2021-12-03
Online:
2022-05-25
Published:
2022-07-25
Supported by:
摘要:
针对高分辨率遥感影像背景复杂,道路提取容易受阴影、建筑物和铁路等背景信息干扰的问题,提出一种带有轻量级双注意力和特征补偿机制的DAFCResUnet模型。该模型在ResUnet的基础上,通过增加轻量级的双注意力和特征补偿模块实现模型在性能和时空复杂度上的平衡。其中,双注意力模块可以增强模型的特征提取能力,特征补偿模块可以融合网络中来自深浅层的道路特征。在DeepGlobe和GF-2道路数据集上的实验结果表明,DAFCResUnet模型的IoU和F1-score可以达到0.6713、0.8033和0.7402、0.8507,模型的整体精度优于U-Net、ResUnet和VNet模型。与U-Net和ResUnet模型相比,DAFCResUnet模型仅增加了少量的计算量和参数量,但IoU和F1-score均有较大幅度的提高;与VNet模型相比,DAFCResUnet模型在计算量和参数量远低于VNet的情况下取得了更高的精度,模型在精度和时空复杂度两方面均有优势。相比其他对比模型,DAFCResUnet模型具有更强的特征提取和抗干扰能力,能更好解决道路上的干扰物、与道路特征相似地物、树荫或阴影遮挡等造成的道路空洞、误提和漏提现象。
陈振, 陈芸芝, 吴婷, 李佳优. 面向高分遥感影像道路提取的轻量级双注意力和特征补偿残差网络模型[J]. 地球信息科学学报, 2022, 24(5): 949-961.DOI:10.12082/dqxxkx.2022.210597
CHEN Zhen, CHEN Yunzhi, WU Ting, LI Jiayou. A Lightweight Dual Attention and Feature Compensated Residual Network Model for Road Extraction from High-Resolution Remote Sensing Images[J]. Journal of Geo-information Science, 2022, 24(5): 949-961.DOI:10.12082/dqxxkx.2022.210597
表1
DAFCResUnet网络各层的参数及输出特征图尺寸
编码器 | 解码器 | ||||||
---|---|---|---|---|---|---|---|
层号 | 网络层 | 步长 | 输出特征图尺寸 | 层号 | 网络层 | 步长 | 输出特征图尺寸 |
1 | Conv 3 | 1 | 256 | 1 | T-Conv 2 | 2 | 32 |
2 | Conv 3 | 1 | 256 | 2 | Conv 3 | 1 | 32 |
3 | Conv 3 | 2 | 128 | 3 | Conv 3 | 1 | 32 |
4 | Conv 3 | 1 | 128 | 4 | T-Conv 2 | 2 | 64 |
5 | Conv 3 | 2 | 64 | 5 | Conv 3 | 1 | 64 |
6 | Conv 3 | 1 | 64 | 6 | Conv 3 | 1 | 64 |
7 | Conv 3 | 2 | 32 | 7 | T-Conv 2 | 2 | 128 |
8 | Conv 3 | 1 | 32 | 8 | Conv 3 | 1 | 128 |
9 | Conv 3 | 2 | 16 | 9 | Conv 3 | 1 | 128 |
10 | Conv 3 | 1 | 16 | 10 | T-Conv 2 | 2 | 256 |
11 | Conv 3 | 1 | 256 | ||||
12 | Conv 3 | 1 | 256 | ||||
13 | Conv 1 | 1 | 256 |
表2
在DeepGlobe测试集上的精度对比
模型 | IoU | Recall | Precision | F1-score | FLOPs(G) | Params(M) |
---|---|---|---|---|---|---|
U-Net[ | 0.6521 | 0.7750 | 0.8044 | 0.7894 | 13.73 | 7.77 |
ResUnet[ | 0.6498 | 0.7748 | 0.8011 | 0.7877 | 14.43 | 8.12 |
VNet[ | 0.6686 | 0.7938 | 0.8091 | 0.8014 | 44.85 | 36.00 |
DAResUnet | 0.6690 | 0.7914 | 0.8122 | 0.8016 | 14.44 | 8.12 |
DAFCResUnet | 0.6713 | 0.7982 | 0.8085 | 0.8033 | 14.46 | 8.12 |
表3
在GF-2测试集上的精度对比
模型 | IoU | Recall | Precision | F1-score | FLOPs(G) | Params(M) |
---|---|---|---|---|---|---|
U-Net[ | 0.7182 | 0.8050 | 0.8695 | 0.8360 | 13.73 | 7.77 |
ResUnet[ | 0.7272 | 0.8109 | 0.8756 | 0.8420 | 14.43 | 8.12 |
VNet[ | 0.7330 | 0.8196 | 0.8741 | 0.8460 | 44.85 | 36.00 |
DAResUnet | 0.7383 | 0.8258 | 0.8745 | 0.8494 | 14.44 | 8.12 |
DAFCResUnet | 0.7402 | 0.8226 | 0.8808 | 0.8507 | 14.46 | 8.12 |
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