| 基于Sentinel-1A合成孔径雷达数据和全卷积网络的城市建设用地监测方法研究 |
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| 引用本文： | 卢文路,刘志锋,何春阳,夏沛.基于Sentinel-1A合成孔径雷达数据和全卷积网络的城市建设用地监测方法研究[J].干旱区地理,2020,43(3):750-760. |
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| 作者姓名： | 卢文路 刘志锋 何春阳 夏沛 |
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| 基金项目： | 国家自然科学基金面上项目（41871185，41971271）；国家自然科学基金创新群体项目（41621061）资助 |
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| 摘 要： | Sentinel-1A合成孔径雷达数据不受云、雾等天气条件的影响并具有丰富的纹理信息，为提取城市建设用地信息提供了一种新的数据源。本文发展了一种基于Sentinel-1A合成孔径雷达数据和全卷积网络的城市建设用地监测方法。该方法的优势主要在于可以有效复合不同极化方式下的Sentinel-1A合成孔径雷达数据和综合集成多尺度特征。在甘肃省张掖市甘州区的应用表明，该方法的提取结果总体精度为92.50%，Kappa系数为0.85。与现有方法“KTH-Pavia城市提取器”相比，Kappa系数提高了37.10%，总体精度提高了11.50%。因此，该方法具有良好的应用潜力。
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| 关 键 词： | Sentinel-1A 哨兵数据 合成孔径雷达 城市建设用地 全卷积网络 深度学习 |
| 收稿时间： | 2019-10-14 |
A new method for detecting urban construction land
based on Sentinel-1A synthetic aperture radar data and fully convolutional
network |
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| LU Wen-lu,LIU Zhi-feng,HE Chun-yang,XIA Pei.A new method for detecting urban construction land
based on Sentinel-1A synthetic aperture radar data and fully convolutional
network[J].Arid Land Geography,2020,43(3):750-760. |
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| Authors: | LU Wen-lu LIU Zhi-feng HE Chun-yang XIA Pei |
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| Abstract: | Timely and accurate extraction of urban construction
land is essential for assessing the impacts of urban expansion on the
environment.Optical remote sensing is susceptible to weather conditions and
contains insufficient information on the texture of urban features,and is thus
not conducive to urban construction land extraction.Synthetic aperture radar
(SAR) is unique in its potential to be used in all weather conditions and at
any time.Furthermore,its backscattering and polarimetric information is
sensitive to the dielectric and geometric properties of the urban land surface,providing
a new means for rapid and accurate extraction of urban construction land.Among
existing SAR data,the Sentinel-1A SAR data is widely used due to its free
access.Nonetheless,existing methods are poorly adapted to fully utilize the
Sentinel 1A SAR data,thereby limiting its application.The fully convolutional
network is a deep learning network developed on the basis of the convolutional
neural network,which adopts “pixel to pixel” image recognition and has become an effective method for extracting
urban construction land.This paper aims to develop a method for detecting urban
construction land based on Sentinel 1A SAR data,and
thus a fully convolutional network.Firstly,a fully convolutional network
consisting of five Inception modules was developed with reference to
GoogleNet.Each Inception module includes three convolutional layers with
convolutional kernel sizes of 1×1,3×3,and 5×5 respectively,three corresponding
activation layers,and a concatenation layer.The Ganzhou District in Zhangye
City,Gansu Province,China,where urban construction land and bare land show
similar spectrum features,was used as a case study area to verify our fully
convolutional network.The extraction results show that the area of construction
land in the Ganzhou District was 4 006 hectares in 2018,accounting for about
20% of the total area.The patch area of urban construction land was generally
found to be between 0.01 and 1 hectare.Urban construction land was mainly
distributed in the west and northeast,and the circle of 2-4 km from the city
center exhibited the most concentrated urban construction land.Our accuracy
assessment shows an overall accuracy of 92.50% and a Kappa coefficient of 0.85.By comparing our results to extraction
results using the “KTH-Pavia” method (the most widely used method for extracting urban construction land from
SAR data),it was found that extraction results based on the fully convolutional
network are closer to the real urban construction land in spatial
patterns.Furthermore,the overall accuracy and Kappa coefficient were,respectively,11% and 37% higher than the “KTH-Pavia” method.The principal reason for
this higher accuracy is that the fully convolution network can better integrate
multi-polarization and multi-scale texture information from SAR
data.Furthermore,the fully convolutional network contains multiple
convolutional structures and supports multi-source data inputs.The method
developed in this study has both greater accuracy than existing methods and is
applicable to urban construction land extraction based on different SAR data in
different regions.It therefore has potential for widespread application. |
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