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Received：22 October 2019，
Revised：2020-2-19，
Accepted：26 February 2020，
Published：16 December 2020
移动端阅览
目的
2
城镇建成区是城镇研究重要的基础信息，也是实施区域规划、落实城镇功能空间布局的前提。但是遥感影像中城镇建成区的环境复杂，同时不同城镇建成区在坐落位置、发展规模等方面存在许多差异，导致其信息提取存在一定困难。
方法
2
本文基于面向图像语义分割的深度卷积神经网络，使用针对特征图的强化模块和通道域的注意力模块，对原始DeepLab网络进行改进，并通过滑动窗口预测、全连接条件随机场处理方法，更准确地实现城镇建成区提取。同时，针对使用深度学习算法容易出现过拟合和鲁棒性不强的问题，采用数据扩充增强技术进一步提升模型能力。
结果
2
实验数据是三亚和海口部分地区的高分二号遥感影像。结果表明，本文方法的正确率高于93%，Kappa系数大于0.837，可以有效地提取出大尺度高分辨率遥感影像中的城镇建成区，且提取结果最为接近实际情况。
结论
2
针对高分辨率遥感卫星影像中城镇建成区的光谱信息多样化、纹理结构复杂化等特点，本文算法能在特征提取网络中获取更多特征信息。本文使用改进的深度学习方法，提出两种处理方法，显著提高了模型的精度，在实际大幅遥感影像的使用中表现优秀，具有重要的实用价值和广阔的应用前景。
Objective
2
The urban built-up area is an important source of basic information for urban research and serves as a prerequisite for the regional planning and implementation of the spatial layout of urban functions. Given the recent developments in Earth observational technologies and improvements in the resolution of remote sensing images
accurately and efficiently extracting information on urban built-up areas has become possible. However
due to the complex environment of urban built-up areas in high-resolution remote sensing images and the variations in their locations and development scales
various forms of remote sensing image representations increase the difficulty of using traditional information extraction methods for urban built-up areas. Recent studies show that deep learning algorithms have significant advantages in processing of large-scale images. This paper then examines these deep learning algorithms and reviews previous research that apply deep convolutional neural network methods
which have been widely used in computer vision to extract information on urban built-up areas from high-resolution satellite images. This article also improves the application of computer image processing technology in the field of remote sensing.
Method
2
Semantic image segmentation is crucial in image processing and computer vision. This process recognizes an image at the pixel level and then labels the object category to which each pixel in the image belongs. Based on the deep convolutional neural network oriented to semantic image segmentation
this paper uses the refinement module for the feature map and the attention module of the channel domain to improve the original DeepLab v3 network. The feature refinement module accurately obtains relevant information between pixels and reduces the grid effect. Afterward
the network model processes the feature map through atrous spatial pyramid pooling. The decoding part of the network extracts the attention information of the channel domain and then weighs the low-level features to achieve a better representation and to restore the detailed information. Afterward
the urban built-up area is extracted via the sliding window prediction and full connection conditional random fields methods
both of which can be applied to extract urban built-up areas with better accuracy. However
the use of deep learning algorithms is prone to overfitting and poor robustness. Accordingly
data augmentation and extension are used to enhance the capabilities of the model. Specifically
we use rotation and filter operations while cutting the original training and verification data into 256×256 samples.
Result
2
Extracting information from remote sensing images involves an effective mining and category judgment of such information. The experimental data are taken from Gaofen-2 remote sensing images of Sanya and Haikou cities in Hainan Province
China. These images are specifically taken at the Qiongshan District of Haikou City and at the Tianya District
Jiyang District
and the sea surrounding the Jiaotouding Island of Sanya City. Given their weak sample processing ability
traditional classification algorithms have achieved an accuracy rate of no higher than 85% in the experiments. Meanwhile
deep learning methods
such as SegNet and DeepLab v3
have relatively high accuracy and better performance in extracting urban built-up area information from remote sensing satellite images. By using the refinement module for the feature map and the attention module of the channel domain
this paper improves the accuracy rate of the original DeepLab v3 network by 1.95%. Meanwhile
the proposed method has an accuracy rate of above 93%
a Kappa coefficient of greater than 0.837
a missed detection rate of less than 4.9%
and a false alarm rate of below 2.1%. This method can effectively extract urban built-up areas from large-scale high spatial resolution remote sensing images
and its extraction results are the closest to the actual situation.
Conclusion
2
The comparative experiment shows that the proposed method outperforms others in extracting urban built-up area information from high-resolution remote sensing satellite imagers with diverse spectral information and complex texture structure. Two processing methods are also proposed to significantly improve the accuracy of the model. Both the sliding window method and conditional random fields processing demonstrate an excellent performance in extracting information from high-resolution remote sensing images and show high application value for large-scale remote sensing images.
Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL].[2019-09-20] . https://arxiv.org/pdf/1412.7062.pdf https://arxiv.org/pdf/1412.7062.pdf
Chen L C, Papandreou G, Schroff F and Adam H. 2017. Rethinking Atrous convolution for semantic image segmentation[EB/OL].[2019-09-20] . https://arxiv.org/pdf/1706.05587.pdf https://arxiv.org/pdf/1706.05587.pdf
Chen L S, Zhao J S, Dong Z W and Zhu Q F. 2018. Urban construction land information extraction based on deep learning by multi-spectral remote sensing imagery of Yunnan central urban agglomeration area. Software Guide, 17(11):177-180, 186
陈磊士, 赵俊三, 董智文, 朱褀夫.基于深度学习的滇中城市多光谱影像建设用地信息提取.软件导刊, 17(11):177-180, 186[DOI:10.11907/rjdk.181447]
Chen Z J, Chen J, Shi P J and Tamura M. 2003. An IHS-based change detection approach for assessment of urban expansion impact on arable land loss in China. International Journal of Remote Sensing, 24(6):1353-1360[DOI:10.1080/0143116021000047910]
Deng L Y, Shen Z F and Ke Y M. 2018. Built-up area extraction and urban expansion analysis based on remote sensing images. Journal of Geo-information Science, 20(7):996-1003
邓刘洋, 沈占锋, 柯映明. 2018.城市建成区遥感影像边界提取与扩张分析.地球信息科学学报, 20(7):996-1003[DOI:10.12082/dqxxkx.2018.170571]
Gao C X and Sang N. 2014. Deep learning for object detection in remote sensing image. Bulletin of Surveying and Mapping, (S1):108-111
高常鑫, 桑农. 2014.基于深度学习的高分辨率遥感影像目标检测.测绘通报, (S1):108-111[DOI:10.13474/j.cnki.11-2246.2014.0625]
Guo L, Ma L and Ye S C. 2012. Remote sensing image extraction of residential areas based on radon and tree-structured wavelet transformation. Geomatics and Spatial Information Technology, 35(1):150-153
过林, 马莉, 叶诗淳. 2012.基于Radon和树型小波变换的遥感影像居民地提取.测绘与空间地理信息, 35(1):150-153[DOI:1672-5867(2012)01-0150-04]
He K M, Zhang X Y, Ren S Q and Jian S. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Hu F, Xia G S, Hu J W and Zhang L P. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11):14680-14707[DOI:10.3390/rs71114680]
Hu J, Shen L and Sun G. 2018. Squeeze-and-excitation networks//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7132-7141[ DOI: 10.1109/CVPR.2018.00745 http://dx.doi.org/10.1109/CVPR.2018.00745 ]
Hu X X, Li Y S, Li H C and Xu Y N. 2010. The comparative analysis about spatial information access technologies based on light UAV low-altitude digital aerophotogrammetry and high-resolution satellite remote sensing. Engineering of Surveying and Mapping, 19(4):68-70, 74
胡晓曦, 李永树, 李何超, 许懿娜. 2010.无人机低空数码航测与高分辨率卫星遥感测图精度试验分析.测绘工程, 19(4):68-70, 74[DOI:10.19349/j.cnki.issn1006-7949. 2010.04.018]
Klonowski J and Koch K R. 1997. Two level image interpretation based on Markov random fields. Semantic Modeling for the Acquisition of Topographic Information from Images and Maps, Germany: Gustav Stresemann Institut in Bonn Bad Godesberg, 37-55
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: ACM: 1097-1105
LeCun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324[DOI:10.1109/5.726791]
Li D R. 2003. Towards the development of remote sensing and GIS in the 21st century. Geomatics and Information Science of Wuhan University, 28(2):127-131
李德仁. 2003.论21世纪遥感与GIS的发展.武汉大学学报·信息科学版, 28(2):127-131[DOI:10.13203/j.whugis2003.02.001]
Li S H, Wang J L, Bi Y, Chen Y, Zhu M Y, Yang S and Zhu J. 2005. A review of methods for classification of remote sensing images. Remote Sensing for Land and Resources, 17(2):1-6
李石华, 王金亮, 毕艳, 陈姚, 朱妙园, 杨帅, 朱佳. 2005.遥感图像分类方法研究综述.国土资源遥感, 17(2):1-6[DOI:10.6046/gtzyyg.2005.02.01]
Lin C G and Nevatia R. 1998. Building detection and description from a single intensity image. Computer Vision and Image Understanding, 72(2):101-121[DOI:10.1006/cviu.1998.0724]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 3431-3440[ DOI: 10.1109/cvpr.2015.7298965 http://dx.doi.org/10.1109/cvpr.2015.7298965 ]
Mnih V and Hinton G E. 2010. Learning to detect roads in high-resolution aerial images//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Greece: Springer: 210-223[ DOI: 10.1007/978-3-642-15567-3_16 http://dx.doi.org/10.1007/978-3-642-15567-3_16 ]
Pesaresi M, Gerhardinger A and Kayitakire F Ç. 2008. A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(3):180-192[DOI:10.1109/jstars.2008.2002869]
Pu R L, Landry S and Yu Q. 2011. Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery. International Journal of Remote Sensing, 32(12):3285-3308[DOI:10.1080/01431161003745657]
Rodriguez-Galiano V F, Ghimire B, Rogan J, Chica-Olmo M and Rigol-Sanchez J P. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67:93-104[DOI:10.1016/j.isprsjprs.2011.11.002]
Rougier S, Puissant A, Stumpf A and Lachiche N. 2016. Comparison of sampling strategies for object-based classification of urban vegetation from very high resolution satellite images. International Journal of Applied Earth Observation and Geoinformation, 51:60-73[DOI:10.1016/j.jag.2016.04.005]
Tao C, Zou Z R and Ding X L. 2014. Residential area detection from high-resolution remote sensing imagery using corner distribution. Acta Geodaetica et Cartographica Sinica, 43(2):164-169, 192
陶超, 邹峥嵘, 丁晓利. 2014.利用角点进行高分辨率遥感影像居民地检测方法.测绘学报, 43(2):164-169, 192[DOI:10.13485/j.cnki.11-2089.2014.0024]
Unsalan C and Boyer K L. 2004. Classifying land development in high-resolution panchromatic satellite images using straight-line statistics. IEEE Transactions on Geoscience and Remote Sensing, 42(4):907-919[DOI:10.1109/tgrs.2003.818835]
Wang J Q, Li J S, Zhou H C and Zhang X. 2019. Typical element extraction method of remote sensing image based on Deeplabv3+ and CRF. Computer Engineering, 45(10):260-265, 271
王俊强, 李建胜, 周华春, 张旭. 2019.基于Deeplabv3+与CRF的遥感影像典型要素提取方法.计算机工程, 45(10):260-265, 271[DOI:10.19678/j.issn.1000-3428.0053359]
Welch R. 1980. Monitoring urban population and energy utilization patterns from satellite data. Remote Sensing of Environment, 9(1):1-9[DOI:10.1016/0034-4257(80)90043-7]
Xia M, Cao G, Wang G Y and Shang Y F. 2017. Remote sensing image classification based on deep learning and conditional random fields. Journal of Image and Graphics, 22(9):1289-1301
夏梦, 曹国, 汪光亚, 尚岩峰. 2017.结合深度学习与条件随机场的遥感图像分类.中国图象图形学报, 22(9):1289-1301[DOI:10.11834/jig.170122]
Xu H Q. 2005. Fast information extraction of urban built-up land based on the analysis of spectral signature and normalized difference index. Geographical Research, 24(2):311-320
徐涵秋. 2005.基于谱间特征和归一化指数分析的城市建筑用地信息提取.地理研究, 24(2):311-320[DOI:10.11821/yj2005020018]
Yang C J and Zhuo C H. 2000. Extracting residential areas on the TM imagery. Journal of Remote Sensing, 4(2):146-150
杨存建, 周成虎. 2000. TM影像的居民地信息提取方法研究.遥感学报, 4(2):146-150[DOI:10.3321/j.issn:1007-4619.2000.02.012]
Yang H W. 2018. High Resolution Remote Sensing Imagery Classification Based on Deep Learning. Beijing: University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
杨惠雯. 2018.基于深度特征学习的高分辨率遥感影像分类.北京: 中国科学院大学(中国科学院遥感与数字地球研究所)
Zhang X Y and Du S H. 2016. Learning selfhood scales for urban land cover mapping with very-high-resolution satellite images. Remote Sensing of Environment, 178:172-190[DOI:10.1016/j.rse.2016.03.015]
Zhao P, Feng X Z and Lin G F. 2003. The decision tree algorithm of automatically extracting residential information from SPOT images. Journal of Remote Sensing, 7(4):309-315
赵萍, 冯学智, 林广发. 2003. SPOT卫星影像居民地信息自动提取的决策树方法研究.遥感学报, 7(4):309-315[DOI:10.11834/jrs.20030413]
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