Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 95-107.doi: 10.12133/j.smartag.SA202206001
张志博1(), 赵西宁2(
), 高晓东2, 张利1, 杨孟豪2
收稿日期:
2022-06-01
出版日期:
2022-09-30
发布日期:
2022-11-23
基金资助:
作者简介:
张志博(1995-),博士研究生,研究方向为农业遥感技术与图像分割。E-mail:通讯作者:
赵西宁
E-mail:zhang_zhibo@nwafu.edu.cn;zxn@nwafu.edu.cn
ZHANG Zhibo1(), ZHAO Xining2(
), GAO Xiaodong2, ZHANG Li1, YANG Menghao2
Received:
2022-06-01
Online:
2022-09-30
Published:
2022-11-23
corresponding author:
ZHAO Xining
E-mail:zhang_zhibo@nwafu.edu.cn;zxn@nwafu.edu.cn
摘要:
黄土高原近20年来苹果栽植面积迅猛增加,对区域生态水文和社会经济发展均产生了重要影响。但该区域果园地块小且场景复杂,仅有县/市尺度统计数据,尚无苹果园实际的空间分布信息。为此,本研究建立了无人机低空遥感影像专业数据集。融合迁移学习与深度学习方法,将残差神经网络ResNet34网络迁移到Linknet网络,得到R_34_Linknet网络。将R_34_Linknet网络与5种常用的深度学习语义分割模型SegNet、FCN_8s、DeeplabV3+、UNet和Linknet应用于黄土高原苹果园空间分布提取,表现最好的模型为R_34_Linknet,其在测试集上的调和平均值F1为87.1%,像素准确度PA为92.3%,均交并比MIoU为81.2%,频权交并比FWIoU为85.7%,平均像素准确度MPA为89.6%。将空间金字塔池化结构(Atrous Spatial Pyramid Pooling,ASPP)与R_34_Linknet网络相结合,扩大网络的感受野,得到R_34_Linknet_ASPP网络;然后对ASPP结构进行改进,得到R_34_Linknet_ASPP+网络。对比三种网络性能,表现最优的为R_34_Linknet_ASPP+,在测试集上F1为86.3%,PA为94.7%,MIoU为82.7%,FWIoU为89.0%,MPA为92.3%。使用R_34_Linknet_ASPP+在长武县王东沟和白水县通积村提取苹果园面积精度分别为94.22%和95.66%。本研究提出的R_34_Linknet_ASPP+方法提取到的苹果园更加准确,苹果园地块边缘处效果更好,可作为黄土高原苹果园空间分布制图等研究的技术支撑和理论依据。
中图分类号:
张志博, 赵西宁, 高晓东, 张利, 杨孟豪. 基于改进Linknet网络的黄土高原苹果园精准提取[J]. 智慧农业(中英文), 2022, 4(3): 95-107.
ZHANG Zhibo, ZHAO Xining, GAO Xiaodong, ZHANG Li, YANG Menghao. Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network[J]. Smart Agriculture, 2022, 4(3): 95-107.
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