基于深度学习的高分辨率遥感影像建筑物提取方法

doi:10.11947/j.AGCS.2019.20170638

测绘学报 ›› 2019, Vol. 48 ›› Issue (1): 34-41.doi: 10.11947/j.AGCS.2019.20170638

基于深度学习的高分辨率遥感影像建筑物提取方法

范荣双^1,2, 陈洋^1,2, 徐启恒³, 王竞雪¹

1. 辽宁工程技术大学测绘与地理科学学院, 辽宁阜新 123000;
2. 中国测绘科学研究院, 北京 100830;
3. 东莞市测绘院, 广东东莞 523129

收稿日期:2017-11-17 修回日期:2018-08-19 出版日期:2019-01-20 发布日期:2019-01-31
通讯作者: 陈洋 E-mail:chenyang1017@126.com
作者简介:范荣双(1975-),男,博士,研究员,研究方向为遥感与地理信息技术应用等。E-mail:fanrsh@casm.casm.ac.cn
基金资助:
国家重点研发计划（2016YFC0803101）；国家自然科学基金（41101452）

A high-resolution remote sensing image building extraction method based on deep learning

FAN Rongshuang^1,2, CHEN Yang^1,2, XU Qiheng³, WANG Jingxue¹

1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100830, China;
3. Dongguan Institute of Surveying and Mapping, Dongguan 523129, China

Received:2017-11-17 Revised:2018-08-19 Online:2019-01-20 Published:2019-01-31
Supported by:
The Nation Key Research and Development Program of China (No. 2016YFC0803101);The National Natural Science Foundation of China (No. 41101452)

摘要/Abstract

摘要：

针对传统的建筑物提取方法精度较低和边界不完整等问题，本文提出基于深度学习的高分辨率遥感影像建筑物提取方法。首先，采用主成分变换非监督预训练网络结构，获得待提取遥感影像特征。其次，为减少在池化过程中影像特征信息的丢失，提出自适应池化模型，通过非下采样轮廓波变换来获取影像纹理特征，并将纹理特征输入网络中参与建筑物提取。最后，将影像特征输入softmax分类器进行分类，获得建筑物提取结果。选取典型区域进行建筑物提取试验，并与典型建筑物提取方法进行对比分析，结果表明，本文提取方法精度高，并且提取建筑物的边界清晰、完整。

关键词: 高分辨率遥感影像, 深度学习, 建筑物信息提取, 自适应池化模型

Abstract:

Traditional building extraction from very high resolution remote sensing optical imagery is limited by low precision and incomplete boundary. In this paper, a high-resolution remote sensing image building extraction method based on deep learning is proposed. Firstly, Principal Component Analysis is used to pre-train network structure in an unsupervised way and obtain the characteristics of remote sensing image. Secondly, an adaptive pooling model is proposed to reduce the feature information loss in the pooling process. The texture features are extracted by non-subsampled contour wave transformation and introduced to the network to improve the building extraction. Finally, the obtained image features are inputted into the softmax classifier for classification and building extraction results. A typical experiment areas selected. The comparison with typical building extraction method, the experimental results shows that the proposed method can extract the buildings with higher accuracy, especially the clearer and more complete boundary.

Key words: high resolution remote sensing image, deep Learning, building information extraction, adaptive pooling model

中图分类号:

P237

范荣双, 陈洋, 徐启恒, 王竞雪. 基于深度学习的高分辨率遥感影像建筑物提取方法[J]. 测绘学报, 2019, 48(1): 34-41.

FAN Rongshuang, CHEN Yang, XU Qiheng, WANG Jingxue. A high-resolution remote sensing image building extraction method based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 34-41.

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基于深度学习的高分辨率遥感影像建筑物提取方法

A high-resolution remote sensing image building extraction method based on deep learning

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