Please wait a minute...
 
Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 48-54     DOI: 10.6046/gtzyyg.2020278
|
Research on building cluster identification based on improved U-Net
WU Yu1(), ZHANG Jun1, LI Yixu2(), HUANG Kangyu1
1. School of Mining, Guizhou University, Guiyang 550025, China
2. College of Agriculture, Guizhou University, Guiyang 550025, China
Download: PDF(3690 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract   Aim

ing at tackling the problem that some edge features of buildings are easily blurred or lost in the extraction of buildings with high resolution image by U-Net, this paper proposes an optimized building extraction method, which firstly enhances the edge of buildings with high resolution image and simultaneously improves the partial convolution process of U-Net. Specific process is as follows: Firstly, the domain change recursive filtering method is used to enhance the edge of the building, and the enhanced image is input into U-Net neural network results for training. To make full use of the rich details characteristics of the buildings on the GF-2 images, the authors tried to extract pairs from training images and label patch on the basis of the original U-Net structure and in the process of coding decoding, so as to increase the training data. These patches further strengthened the positive and negative deep learning of high-dimensional feature for buildings, thus successfully realizing building image segmentation. In this paper, the experimental results of the extraction of GF-2 image buildings in Panjin City of Liaoning Province adjacent to Bohai Bay on September 29, 2017 show that the overall classification accuracy of the buildings detected by U-Net is 75.99% for the shaded and unsatisfied area sample data, and the maximum overall classification accuracy of this method can reach 83.12%, which is 7.13 percentage higher than that of the original U-Net network. It is proved that the U-NET model combined with domain change recursive filtering is effective.
Keywords deep learning      domain change recursive filtering      U-Net      edge enhancement      building extraction     
ZTFLH:  TP751  
Corresponding Authors: LI Yixu     E-mail: ywu@niglas.ac.cn;lyxgis@163.com
Issue Date: 21 July 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yu WU
Jun ZHANG
Yixu LI
Kangyu HUANG
Cite this article:   
Yu WU,Jun ZHANG,Yixu LI, et al. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020278     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/48
Fig.1  Flow chart of the experiment
Fig.2  Experimental samples
Fig.3  Filtering effect with different parameters
Fig.4  Influence of different parameters on DTRF-Unet output data
δs δr
0.2 0.3 0.4 0.5 0.6 0.7 0.8
30 80.41 81.94 82.65 83.12 82.96 82.50 81.83
45 80.42 81.96 82.63 83.04 82.86 82.37 81.49
60 80.46 81.97 82.60 83.00 82.82 82.25 81.25
Tab.1  Overall classification accuracy of DTRF-Unet under different parameters(%)
Fig.5  Comparison of results before and after improvement
[1] 王伟鹏, 戴声奎. 基于域变换递归滤波的雾天图像复原[J]. 计算机应用研究, 2015, 32(2):638-640.
[1] Wang W P, Dai S K. Haze image restoration using domain transform recursive filter[J]. Computer Application Research, 2015, 32(2):638-640.
[2] 彭海涛, 柯长青. 基于多层分割的面向对象遥感影像分类方法研究[J]. 遥感技术与应用, 2010, 25(1):149-154.
[2] Peng H T, Ke C Q. Study on object-oriented remote sensing image classification based on multi levels segmentation[J]. Remote Sensing Technology and Application, 2010, 25(1)149-154.
[3] Camps-Valls G, Gomez-Chova L, Munoz-Mari J, et al. Composite kernels for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1):93-97.
doi: 10.1109/LGRS.2005.857031 url: http://ieeexplore.ieee.org/document/1576697/
[4] 沈毅. 高分辨率遥感图像处理若干关键技术研究[D]. 南京:南京航空航天大学, 2012.
[4] Shen Y. Research on some key technologies of high-resolution remote sensing image processing[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2012.
[5] 眭海刚, 冯文卿, 李文卓, 等. 多时相遥感影像变化检测方法综述[J]. 武汉大学学报(信息科学版), 2018, 43(12):1885-1898.
[5] Sui H G, Feng W Q, Li W Z, et al. Review of change detection methods for multi-temporal remote sensing imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1885-1898.
[6] Baltsavias E P. Object extraction and revision by image analysis using existing geodata and knowledge:Current status and steps towards operational systems[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2004, 58(3/4):129-151.
[7] Alarcon-Ramirez A, Rwebangira M R, Chouikha M F, et al. A new methodology based on level sets for target detection in hyperspectral images[J]. IEEE Transactions on Geoence & Remote Sensing, 2016, 54(9):5385-5396.
[8] Wei G, Wen Y, Haijian Z, et al. Geospatial object detection in high resolution satellite images based on multi-scale convolutional neural network[J]. Remote Sensing, 2018, 10(1):131.
doi: 10.3390/rs10010131 url: http://www.mdpi.com/2072-4292/10/1/131
[9] 方旭, 王光辉, 杨化超, 等. 结合均值漂移分割与全卷积神经网络的高分辨遥感影像分类[J]. 激光与光电子学进展, 2018, 55(2):022802.
[9] Fang X, Wang G H, Yang H C, et al. High resolution remote sensing image classification combined with mean shift segmentation and full convolutional neural network[J]. Advances in Laser and Optoelectronics, 2018, 55(2):022802.
[10] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems, 2012, 60(2):1097-1105.
[11] Mou L, Ghamisi P, Zhu X X. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transaction Geoscience and Remote Sensing, 2017, 55(7):3639-3655.
doi: 10.1109/TGRS.2016.2636241 url: http://ieeexplore.ieee.org/document/7914752/
[12] 陈睿敏, 孙胜利. 基于深度学习的红外遥感信息自动提取[J]. 红外, 2017, 38(8):37-43.
[12] Chen R M, Sun S L. Automatic extraction of infrared remote sensing information based on deep learning[J]. Infrared, 2017, 38(8):37-43.
[13] 王宁, 程家骅, 张寒野, 等. U-net模型在高分辨率遥感影像水体提取中的应用[J]. 国土资源遥感, 2020, 32(1):35-42.doi: 10.6046/gtzyyg.2020.01.06.
doi: 10.6046/gtzyyg.2020.01.06
[13] Wang N, Cheng J H, Zhang H Y, et al. Application of U-net model to water extraction with high resolution remote sensing data[J]. Remote Sensing for Land and Resources, 2020, 32(1):35-42.doi: 10.6046/gtzyyg.2020.01.06.
doi: 10.6046/gtzyyg.2020.01.06
[14] 李越帅, 郑宏伟, 罗格平, 等. 集成U-Net方法的无人机影像胡杨树冠提取和计数[J]. 遥感技术与应用, 2019, 34(5):939-949.
[14] Li Y S, Zhen H W, Luo G P, et al. Extraction and counting of Populus Euphratica crown using UAV images integrated with U-Net method[J]. Remote Sensing Technology and Application, 2019, 34(5):939-949.
[15] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]// IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2015, 79(11):3431-3440.
[16] 苏健民, 杨岚心, 景维鹏. 基于U-Net的高分辨率遥感图像语义分割方法[J]. 计算机工程与应用, 2019, 55(7):207-213.
[16] Su J M, Yang L X, Jing W P. U-Net based semantic segmentation method for high resolution remote sensing image[J]. Computer Engineering and Applications, 2019, 55(7):207-213.
[17] 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望[J]. 自动化学报, 2017, 43(8):1289-1305.
[17] Zhang H, Wang K F, Wang F Y. Advances and perspectives on applications of deep learning in visual object detection[J]. Journal of Automation, 2017, 43(8):1289-1305.
[18] 伍广明, 陈奇, Shibasaki R, 等. 基于U型卷积神经网络的航空影像建筑物检测[J]. 测绘学报, 2018, 47(6):864-872.
[18] Wu G M, Chen Q, Shibasaki R, et al. High precision building detection from aerial imagery using a U-Net like convolutional architecture[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):864-872.
[19] 向轩琳. 基于域变换递归滤波的高光谱图像分类方法研究[D]. 长沙:湖南大学, 2018.
[19] Xiang X L. Research on hyperspectral image classification method based on domain transform recursive filter[D]. Changsha:Hunan University, 2018.
[20] Gastal E S L, Oliveira M M. Domain transform for edge-aware image and video processing[C]// ACM Transactions on Graphics. 2011, 30(4):1-12.
[21] Kang X D, Duan P H, Li S T. Hyperspectral image visualization with edge-preserving filtering and principal component analysis[J]. Information Fusion, 2020, 57:130-143.
doi: 10.1016/j.inffus.2019.12.003 url: https://linkinghub.elsevier.com/retrieve/pii/S1566253518303051
[22] Ronneberger O, Fischer P, Brox T. U-Net:Convolutional networks for biomedical image segmentation[C]// Medical Image Computing and Computer-Assisted Intervention.Springer, 2015:234-241.
[23] Badrinarayanan V, Kendall A, Cipolla R. SegNet:A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(12):2481-2495.
[24] 张浩然, 赵江洪, 张晓光. 利用U-net网络的高分遥感影像建筑提取方法[J]. 遥感信息, 2020, 35(3):143-150.
[24] Zhang H R, Zhao J H, Zhang X G. High resolution remote sensing image building extraction method using U-net network[J]. Remote Sensing Information, 2020, 35(3):143-150.
[25] Li R, Liu W, Yang L, et al. DeepUNet:A deep fully convolutional network for pixel-level sea-land segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2018, 11(11):3954-3962.
[26] 郝建亭, 杨武年, 李玉霞, 等. 基于FLAASH的多光谱影像大气校正应用研究[J]. 遥感信息, 2008(1):78-81.
[26] Hao J T, Yang W N, Li Y X, et al. Atmospheric correction of multi-spectral imagery ASTER[J]. Remote Sensing Information, 2008(1):78-81.
[1] LIU Li, DONG Xianmin, LIU Juan. A performance evaluation method for semantic segmentation models of remote sensing images considering surface features[J]. Remote Sensing for Natural Resources, 2023, 35(3): 80-87.
[2] NIU Xianghua, HUANG Wei, HUANG Rui, JIANG Sili. A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion[J]. Remote Sensing for Natural Resources, 2023, 35(3): 116-123.
[3] LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
[4] ZHANG Xian, LI Wei, CHEN Li, YANG Zhaoying, DOU Baocheng, LI Yu, CHEN Haomin. Research progress and prospect of remote sensing-based feature extraction of opencast mining areas[J]. Remote Sensing for Natural Resources, 2023, 35(2): 25-33.
[5] DIAO Mingguang, LIU Yong, GUO Ningbo, LI Wenji, JIANG Jikang, WANG Yunxiao. Mask R-CNN-based intelligent identification of sparse woods from remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 97-104.
[6] QIU Lei, ZHANG Xuezhi, HAO Dawei. VideoSAR moving target detection and tracking algorithm based on deep learning[J]. Remote Sensing for Natural Resources, 2023, 35(2): 157-166.
[7] HU Jianwen, WANG Zeping, HU Pei. A review of pansharpening methods based on deep learning[J]. Remote Sensing for Natural Resources, 2023, 35(1): 1-14.
[8] ZHAO Linghu, YUAN Xiping, GAN Shu, HU Lin, QIU Mingyu. An information extraction model of roads from high-resolution remote sensing images based on improved Deeplabv3+[J]. Remote Sensing for Natural Resources, 2023, 35(1): 107-114.
[9] ZHANG Ke, ZHANG Gengsheng, WANG Ning, WEN Jing, LI Yu, YANG Jun. A forecasting method for water table depths in areas with power transmission lines based on remote sensing and deep learning models[J]. Remote Sensing for Natural Resources, 2023, 35(1): 213-221.
[10] LYU Yanan, ZHU Hong, MENG Jian, CUI Chengling, SONG Qiqi. A review and adaptability study of deep learning models for vehicle detection based on high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(4): 22-32.
[11] TAN Hai, ZHANG Rongjun, FAN Wenfeng, ZHANG Yifang, XU Hang. Classification and detection of radiation anomalies in Chinese optical satellite images by integrating multi-scale features[J]. Remote Sensing for Natural Resources, 2022, 34(4): 97-104.
[12] SU Wei, LIN Yangyang, YUE Wen, CHEN Yingbiao. Identification of mariculture areas in Guangdong Province and remote sensing monitoring of their spatial and temporal changes based on the U-Net convolutional neural network[J]. Remote Sensing for Natural Resources, 2022, 34(4): 33-41.
[13] ZHANG Pengqiang, GAO Kuiliang, LIU Bing, TAN Xiong. Classification of hyperspectral images based on deep Transformer network combined with spatial-spectral information[J]. Remote Sensing for Natural Resources, 2022, 34(3): 27-32.
[14] ERPAN Anwar, MAMAT Sawut, MAIHEMUTI Balati. Recognition of cotton distribution based on GF-2 images and Unet model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 242-250.
[15] SUN Yu, HUANG Liang, ZHAO Junsan, CHANG Jun, CHEN Pengdi, CHENG Feifei. High spatial resolution automatic detection of bridges with high spatial resolution remote sensing images based on random erasure and YOLOv4[J]. Remote Sensing for Natural Resources, 2022, 34(2): 97-104.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech