地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (4): 692-709.doi: 10.12082/dqxxkx.2021.200130
唐璎1,2,3,4, 刘正军1,*, 杨懿1, 顾海燕1, 杨树文2,3,4
收稿日期:
2020-03-21
修回日期:
2020-06-21
出版日期:
2021-04-25
发布日期:
2021-06-25
通讯作者:
*刘正军(1974— ),男,湖南湘潭人,研究员,主要从事遥感影像信息提取与生态环境遥感监测、突发事件应急地 理信息技术等研究。E-mail: zjliu@casm.ac.cn基金资助:
TANG Ying1,2,3,4, LIU Zhengjun1,*, YANG Yi1, GU Haiyan1, YANG Shuwen2,3,4
Received:
2020-03-21
Revised:
2020-06-21
Online:
2021-04-25
Published:
2021-06-25
Supported by:
摘要:
近年来,城市发展快速,大量人口奔向城市工作生活,城市建筑物的数量有如雨后春笋般扩张,需要合理地规划城市土地资源,遏制违规乱建现象,因此基于高分辨率遥感影像,对建筑物进行准确提取,对城市规划和管理有着重要辅助作用。本文基于U-Net网络模型,使用美国马萨诸塞州建筑物数据集,对网络模型结构进行探究,提出了一种激活函数为ELU、“编码器-特征增强-解码器”结构的网络模型FE-Net。实验首先通过比较不同网络层数的U-Net5、U-Net6、U-Net7的建筑物提取效果,找到最佳的基础网络模型U-Net6;其次,基于该模型,加入特征增强结构得到“U-Net6+ReLU+特征增强”的网络模型;最后,考虑到ReLU容易产生神经元死亡,为优化激活函数,将激活函数替换为ELU,从而得到网络模型FE-Net(U-Net6+ELU+特征增强)。比较3个网络模型(U-Net6+ReLU、U-Net6+ReLU+特征增强、FE-Net(U-Net6+ELU+特征增强))的建筑物提取结果,表明FE-Net网络模型的建筑物提取效果最好,精度放松F1值达到97.23%,比“U-Net6+ReLU”和“U-Net6+ReLU+特征增强”2个网络模型分别高出0.36%和0.12%,且与其他具有相同数据集的研究成果比较,具有最高的提取精度,它能较好地提取出多尺度的建筑物,不仅对小尺度建筑物有较好的提取效果,而且能大致、较完整地提取出形状不规则的建筑物,有相对更少的漏检和错检,较准确地实现了端到端的建筑物提取。
唐璎, 刘正军, 杨懿, 顾海燕, 杨树文. 基于特征增强和ELU的神经网络建筑物提取研究[J]. 地球信息科学学报, 2021, 23(4): 692-709.DOI:10.12082/dqxxkx.2021.200130
TANG Ying, LIU Zhengjun, YANG Yi, GU Haiyan, YANG Shuwen. Research on Building Extraction based on Neural Network with Feature Enhancement and ELU Activation Function[J]. Journal of Geo-information Science, 2021, 23(4): 692-709.DOI:10.12082/dqxxkx.2021.200130
表1
FE-Net网络模型中各个特征图的对应参数
编码器部分 | 解码器部分 | ||
---|---|---|---|
名称 | 尺寸和通道数 | 名称 | 尺寸和通道数 |
输入影像 | 384×384×3 | 特征图 | 12×12×1024 |
Conv(3×3), 32 | 384×384×32 | Transposed Conv(3×3), m×2+拼接 | 24×24×1024 |
Conv(3×3), n×1 | 384×384×32 | Conv(3×3), n×(1/2) | 24×24×512 |
Maxpool(2×2), m×(1/2) | 192×192×32 | Conv(3×3), n×1 | 24×24×512 |
Conv(3×3), n×2 | 192×192×64 | Transposed Conv(3×3), m×2+拼接 | 48×48×512 |
Conv(3×3), n×1 | 192×192×64 | Conv(3×3), n×(1/2) | 48×48×256 |
Maxpool(2×2), m×(1/2) | 96×96×64 | Conv(3×3), n×1 | 48×48×256 |
Conv(3×3), n×2 | 96×96×128 | Transposed Conv(3×3), m×2+拼接 | 96×96×256 |
Conv(3×3), n×1 | 96×96×128 | Conv(3×3), n×(1/2) | 96×96×128 |
Maxpool(2×2), m×(1/2) | 48×48×128 | Conv(3×3), n×1 | 96×96×128 |
Conv(3×3), n×2 | 48×48×256 | Transposed Conv(3×3), m×2+拼接 | 192×192×128 |
Conv(3×3), n×1 | 48×48×256 | Conv(3×3), n×(1/2) | 192×192×64 |
Maxpool(2×2), m×(1/2) | 24×24×256 | Conv(3×3), n×1 | 192×192×64 |
Conv(3×3), n×2 | 24×24×512 | Transposed Conv(3×3), m×2+拼接 | 384×384×64 |
Conv(3×3), n×1 | 24×24×512 | Conv(3×3), n×(1/2) | 384×384×32 |
Maxpool(2×2), m×(1/2) | 12×12×512 | Conv(3×3), n×1 | 384×384×32 |
Conv(3×3), n×2 | 12×12×1024 | Conv(3×3), 1 | 384×384×1 |
Conv(3×3), n×1 | 12×12×1024 |
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