Research on new construction land information extraction based on deep learning: Innovation exploration of the national project of land use monitoring via remote sensing

doi:10.6046/gtzyyg.2019.04.21

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(2883 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The national project of land use monitoring via remote sensing has created millions of samples of new construction land. Based on these data, the authors conducted a preliminary research on applying the deep learning technology to automatically detect new constructions in comparison with the results generated by specialists. This study demonstrates that the deep learning technology has a great potential for completing the task of monitoring land use via remote sensing. It is believed that the efficiency of the project would be increased dramatically with minor manual assistance when a recall accuracy reaches 80%.

Keywords deep learning land use remote sensing monitoring new construction land

TP79

Issue Date: 03 December 2019

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Haiping WU
	Shicun HUANG

Cite this article:

Haiping WU,Shicun HUANG. Research on new construction land information extraction based on deep learning: Innovation exploration of the national project of land use monitoring via remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(4): 159-166.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.21 OR https://www.gtzyyg.com/EN/Y2019/V31/I4/159

Tab.1 Classification system of new constructed land

Fig.1 Flowchart of deep learning method

Tab.2 Evaluation of accuracy by deep learning

Fig.2 Result of land use changes detecting based on deep learning

Tab.3 Statistics of new constructed land of Tongzhou District

Tab.4 Statistics of new constructed land of Yanjiang District

Tab.5 Accuracy of different types based on TongZhou(%)

Tab.6 Accuracy of different types based on YanJiang(%)

[1]	Hinton G E, Salakhutdinov R . Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(5786):504-507.
[2]	Mnih V, Hinton G E. Learning to detect roads in high resolution aerial images [C]//Proc.of 2010 European Conf.Computer Vision(ECCV2010).Piscataway,NJ:IEEE, 2010: 210-223.
[3]	刘大伟, 韩玲, 韩晓勇 . 基于深度学习的高分辨率遥感影像分类研究[J].光学学报,2016(4):306-314.
[3]	Liu D W, Han L, Han X Y . High spatial resolution remote sensing image classification based on deep learning[J].Acta Optica Sinica,2016(4):306-314.
[4]	高常鑫, 桑农 . 基于深度学习的高分辨率遥感影像目标检测[J].测绘通报,2014(s1):108-111.
[4]	Gao C X, Sang N . Deep learning for object detection in remote sensing image[J].Bulletin of Surveying and Mapping,2014(s1):108-111.
[5]	韩洁, 李盛阳, 张涛 . 基于深度学习的遥感影像城市扩展方法研究[J]. 载人航天, 2017,23(3):414-419.
[5]	Han J, Li SY, Zhang T . Research on urban expansion method based on deep learning of remote sensing image[J]. Manned Spaceflight, 2017,23(3):414-419.
[6]	张鑫龙, 陈秀万, 李飞 , 等. 高分辨率遥感影像的深度学习变化检测方法[J]. 测绘学报, 2017,46(8):999-1008.
[6]	Zhang X L, Chen X W, Li F , et al. Change detection method for high resolution remote sensing images using deep learning[J]. Acta Geodaeticaet Cartographica Sinica, 2017,46(8):999-1008.
[7]	阚希, 张永宏, 曹庭 , 等. 利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识[J]. 测绘学报, 2016,45(10):1210-1221.
[7]	Kan X, Zhang Y H, Cao T , et al. Snow cover recognition for Qinghai-Tibetan Plateau using deep learning and multispectral remote sensing[J]. Acta Geodaeticaet Cartographica Sinica, 2016,45(10):1210-1221.
[8]	陈洋, 范荣双, 王竞雪 , 等. 基于深度学习的资源三号卫星遥感影像云检测方法[J]. 光学学报, 2018,38(1):354-359.
[8]	Chen Y, Fan R S, Wang J X , et al. Cloud detection of ZY-3 satellite remote sensing images based on deep learning[J]. Acta Optica Sinica, 2018,38(1):354-359.
[9]	朱祺夫, 赵俊三, 陈磊士 , 等. 基于深度学习的遥感影像城市建筑用地提取[J] 软件导刊, 2018,17(10):18-21.
[9]	Zhu Q F, Zhao J S, Chen L S , et al. Urban construction land extraction of the remote sensing image based on depth learning[J]. Software Guide, 2018,17(10):18-21.
[10]	常亮, 邓小明, 周明全 , 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016,42(9):1300-1312. doi: 10.16383/j.aas.2016.c150800
[10]	Chang L, Deng X M, Zhou M Q . Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 2016,42(9):1300-1312.
[11]	朱寿红, 王胜利, 舒帮荣 . 基于深度学习的高光谱遥感影像分类[J].城市勘测,2017(4):84-88.
[11]	Zhu S H, Wang S L, Shu B R . Classification of hyperspectral remote sensing image based on deep learning[J]Urban Geotechnical Investigation & Surveying, 2017(4):84-88.
[12]	余凯, 贾磊, 陈宇强 , 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013,50(9):1799-1804.
[12]	Yu K, Jia L, Chen Y Q , et al. Yesterday,today and tomorrow of deep learning[J]. Computer Research and Development, 2013,50(9):1799-1804.
[13]	郭丽丽, 丁世飞 . 深度学习研究进展[J]. 计算机科学, 2015,42(5):28-33.
[13]	Guo L L, Ding S F . Research progress of deep learning[J]. Computer Science, 2015,42(5):28-33.
[14]	黄昕 . 高分辨率遥感影像多尺度纹理、形状特征提取与面向对象分类研究[D]. 武汉:武汉大学, 2009: 71-76.
[14]	Huang X , Multiscale Texture and Shape Feature Extraction and Object-oriented Classification for Very High Resolution Remotely Sensed Imagery[D]. Wuhan:Wuhan University, 2009: 71-76.
[15]	Hinton G . A practical guide to training restricted Boltzmann machines[J]. Momentum, 2010,9(1):926.
[16]	Larochelle H, Bengio Y, Louradour J , et al. Exploring strategies for training deep neural networks[J]. Journal of Machine Learning Research, 2009,10(1):1-40.

[1]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[2]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[3]	SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[4]	GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[5]	WU Jiang, LIU Chun, YING Shen, YU Ting. Spatial delineation methods of urban areas[J]. Remote Sensing for Natural Resources, 2021, 33(4): 89-97.
[6]	SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[7]	XIAO Dongsheng, LIAN Hong. Population spatialization based on geographically weighted regression model considering spatial stability of parameters[J]. Remote Sensing for Natural Resources, 2021, 33(3): 164-172.
[8]	DENG Xiaojin, JING Changqing, GUO Wenzhang, Yan Yujiang, CHEN Chen. Surface albedos of different land use types in the Junggar Basin[J]. Remote Sensing for Natural Resources, 2021, 33(3): 173-183.
[9]	WANG Qingchuan, XI Yantao, LIU Xinran, ZHOU Wen, XU Xinran. Spatial-temporal response of ecological service value to land use change: A case study of Xuzhou City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 219-228.
[10]	FENG Dongdong, ZHANG Zhihua, SHI Haoyue. Fine extraction of urban villages in provincial capitals based on multivariate data[J]. Remote Sensing for Natural Resources, 2021, 33(3): 272-278.
[11]	SONG Qi, FENG Chunhui, GAO Qi, WANG Mingyue, WU Jialin, PENG Jie. Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years[J]. Remote Sensing for Land & Resources, 2021, 33(2): 202-212.
[12]	CHEN Dong, YAO Weiling. Automatic numbering and method improvement of mine patches based on ArcPy and custom ArcToolbox[J]. Remote Sensing for Land & Resources, 2021, 33(2): 262-269.
[13]	WU Yu, ZHANG Jun, LI Yixu, HUANG Kangyu. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
[14]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[15]	AN Jianjian, MENG Qingyan, HU Die, HU Xinli, YANG Jian, YANG Tianliang. The detection and determination of the working state of cooling tower in the thermal power plant based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2021, 33(2): 93-99.

Viewed

Full text

Abstract

Cited

Shared

Discussed