融合多特征改进型PSPNet模型应用于复杂场景下的建筑物提取

doi:10.13474/j.cnki.11-2246.2021.0170

测绘通报 ›› 2021, Vol. 0 ›› Issue (6): 21-27.doi: 10.13474/j.cnki.11-2246.2021.0170

融合多特征改进型PSPNet模型应用于复杂场景下的建筑物提取

武花¹, 张新长¹, 孙颖², 蔡伟男¹, 颜军⁴, 邓剑文⁴, 张建国³

1. 广州大学地理科学与遥感学院, 广东广州 510006;
2. 中山大学地理科学与规划学院, 广东广州 510275;
3. 湖南博通信息股份有限公司, 湖南长沙 410007;
4. 珠海欧比特宇航科技股份有限公司, 广东珠海 519000

收稿日期:2021-03-08 发布日期:2021-06-28
通讯作者: 张新长。E-mail:eeszxc@mail.sysu.edu.cn
作者简介:武花(1995—),女,硕士生,主要研究方向为基于深度学习的遥感影像地物识别。E-mail:1605308813@qq.com
基金资助:
国家重点研发计划（2018YFB2100702）；国家自然科学基金（41801351）；企事业单位委托科技项目（521023）；微纳高光谱卫星数据自动智能一体化地面处理系统建设（ZH0405-1900-01PWC）

Building extraction in complex scenes based on the fusion of multi-feature improved PSPNet model

WU Hua¹, ZHANG Xinchang¹, SUN Ying², CAI Weinan¹, YAN Jun⁴, DENG Jianwen⁴, ZHANG Jianguo³

1. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China;
2. Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;
3. Hunan Botong Information Co., Ltd., Changsha 410007, China;
4. Zhuhai ORBITA Aerospace Science & Technology Co., Ltd., Zhuhai 519000, China

Received:2021-03-08 Published:2021-06-28

摘要/Abstract

摘要： 针对复杂场景下高分辨率遥感影像中建筑物提取精度低的问题，本文提出了一种融合多特征改进型PSPNet模型，在PSPNet网络的基础上，加入膨胀卷积模块并融合图像的浅层特征。试验结果表明，融合多特征改进型PSPNet模型的预测结果总体精度为95.90%，建筑物提取精度平均为77.77%，均高于其他模型。其在不同场景上的表现有所差异：复杂场景1的预测精度为80.35%；以城中村建筑物为主的场景2的预测精度为75%；以高层建筑物为主的场景3的预测精度为78.11%。因此本模型可有效地提升高分辨率遥感影像中复杂场景下的建筑物提取精度。

关键词: 语义分割, 建筑物提取, PSPNet, 膨胀卷积, 金字塔池化模块

Abstract: Aiming at the problem of low accuracy of building extraction in complex scenes of high-resolution remote sensing images, this paper proposes an improved PSPNet model which integrates multiple features. On the basis of PSPNet network, the expansion convolution module is added and the shallow features of the image are fused. The results show that the overall prediction accuracy by the improved PSPNet model is 95.90%, and the average building extraction accuracy is 77.77%, which is higher than other models. It varies in performance from scene to scene. In the first scene that is complex the prediction accuracy is as high as 80.35%; in the second scene with village buildings in the city, the prediction accuracy is 75%; in the third scene with high-rise buildings, the prediction accuracy is 78.11%. This model can effectively improve the extraction accuracy of buildings in complex scenes of high-resolution remote sensing images.

Key words: semantic segmentation, building extraction, PSPNet, dilated convolution, pyramid pooling moduel

中图分类号:

P237

武花, 张新长, 孙颖, 蔡伟男, 颜军, 邓剑文, 张建国. 融合多特征改进型PSPNet模型应用于复杂场景下的建筑物提取[J]. 测绘通报, 2021, 0(6): 21-27.

WU Hua, ZHANG Xinchang, SUN Ying, CAI Weinan, YAN Jun, DENG Jianwen, ZHANG Jianguo. Building extraction in complex scenes based on the fusion of multi-feature improved PSPNet model[J]. Bulletin of Surveying and Mapping, 2021, 0(6): 21-27.

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融合多特征改进型PSPNet模型应用于复杂场景下的建筑物提取

Building extraction in complex scenes based on the fusion of multi-feature improved PSPNet model

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