深度学习和遥感影像支持的矢量图斑地类解译真实性检查方法

doi:10.12082/dqxxkx.2020.200001

Abstract

Abstract:

Spatial data quality check is important to guarantee the accuracy and reliability of remote sensing data. Current spatial data quality check mainly focuses on topological relationship, attribute consistency, and data correlation, ignoring the authenticity of land type interpretation in vector patches. Therefore, this paper proposes a method of checking the authenticity of land type interpretation in vector patches using deep learning and high-resolution remote sensing images, which is designed to meet the urgent need of authenticity check for vector patch categories in major national and industrial projects. In this paper, a method of automatic sample labeling and purification is proposed, which uses the geographic location information of the original map spot to realize processing unit labels automatically and uses two different classifiers combined with cross validation to achieve automatic sample purification, to obtain a large number of high-quality labeled samples for deep learning model training. The classic deep learning model (i.e., Inception_v3) is used for transfer learning to classify segmented images into automatic scene classifications. The overall classification accuracy of the deep learning model in the study area reaches 0.934, the average precision is 0.937, and the average recall is 0.928. Based on the scene classification results using high-resolution remote sensing images, the original vector patches are overlapped and analyzed. By connecting related fields, the segmentation units with inconsistent category information are automatically identified as suspicious patches. We further verified the authenticity check method of land type interpretation of vector patches in Qingshan Quan Town and Dawu Town, Jiawang District, and Xuzhou City. The experimental results show that the precision and recall of our method for these study areas are as high as 0.925 and 0.817, respectively. The method proposed in this paper provides a useful technical support for the check of the authenticity of the land type interpretation of vector patches.

Key words: deep learning, Inception_v3, high resolution remote sensing image, spatial data quality check, vector patch, transfer learning, scene classification, authenticity

GUO Zihui, LIU Wei. Land Type Interpretation Authenticity Check of Vector Patch Supported by Deep Learning and Remote Sensing Image[J].Journal of Geo-information Science, 2020, 22(10): 2051-2061.DOI:10.12082/dqxxkx.2020.200001

Figures/Tables 18

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 1

Tab. 2

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Tab. 3

Fig. 9

Fig. 10

Fig. 11

Tab. 4

Fig. 12

Tab. 5

Fig. 13

References 21

[1]	石高兵. 矢量数字地图数据质量检查内容及影响质量因素分析[J]. 北京测绘, 2015(5):110-112.
	[ Shi G B. Quality inspection content of vector digital map data and analysis of factors affecting quality[J]. Beijing Surveying and Mapping, 2015(5):110-112. ]
[2]	吴长彬, 闾国年, 舒飞跃. 基于知识与规则的地籍数据质量检查方法[J]. 地理与地理信息科学, 2007,23(5):22-25,30.
	[ Wu C B, Lu G N, Shu F Y. Quality inspection method of cadastral data based on knowledge and rules[J]. Geography and Geographic Information Science, 2007,23(5):22-25,30. ]
[3]	覃东华. 基于FME的地理国情监测矢量数据接边检查方法的探讨[J]. 城市勘测, 2018(5):42-44,48.
	[ Qin D H. Discussion on the method of edge checking for vector data of geographical situation monitoring based on FME[J]. Urban Survey, 2018(5):42-44,48. ]
[4]	钱海忠, 武芳, 郭健, 等. 基于制图综合知识的空间数据检查[J]. 测绘学报, 2006(2):184-190.
	[ Qian H Z, Wu F, Guo J, et al. Spatial data checking based on cartographic generalization knowledge[J]. Journal of Surveying and mapping, 2006(2):184-190. ]
[5]	刘建军, 王东华, 商瑶玲. 国家1:50 000 DLG数据库质量检查软件的设计和开发[J]. 地理信息世界, 2006(4):23-26.
	[ Liu J J, Wang D H, Shang Y L. Design and development of national 1:50 000 DLG database quality inspection software[J]. World of Geographic Information, 2006 (4):23-26. ]
[6]	潘重阳. 浅谈Geoway在矢量数据检查中的应用[A]. 中国测绘学会科技信息网分会. 全国测绘科技信息网中南分网第二十八次学术信息交流会论文集[C]. 中国测绘学会科技信息网分会:中国测绘学会科技信息网分会, 2014: 4.
	[ Pan C Y. On Geoway's application in vector data inspection[A]. Science and technology information network branch of China Society of Surveying and mapping. Proceedings of the 28th academic information exchange meeting of South Central Branch of national science and technology information network of Surveying and mapping[C]. Science and technology information network branch of China Society of Surveying and mapping: Science and technology information network branch of China Society of Surveying and mapping, 2014: 4. ]
[7]	张晏. 《第三次全国土地调查总体方案》发布[J]. 资源导刊, 2018(1):6.
	[ Zhang Y. The Third General Plan of National Land Survey was published[J]. Resource Guide, 2018(1):6. ]
[8]	政策[J]. 资源导刊, 2018(12):5.
	[ Policy[J]. Resource Guide, 2018(12):5. ]
[9]	孙志军, 薛磊, 许阳明, 等. 深度学习研究综述[J]. 计算机应用研究, 2012,29(8):2806-2810.
	[ Sun Z J, Xue L, Xu Y M, et al. A review of deep learning research[J]. Computer Application Research, 2012,29(8):2806-2810. ]
[10]	李翔宇, 孙晓庆. 基于深度学习的图像分类分析研究[J]. 中国信息化, 2019(4):48-50.
	[ Li X Y, Sun X Q. Image classification and analysis based on deep learning[J]. China informatization, 2019(4):48-50. ]
[11]	Liu W, Yang M Y, Xie M, et al. Accurate building extraction from fused DSM and UAV images using a chain fully convolutional neural network[J]. Remote Sensing, 2019,11(24):2912.
[12]	罗希平, 田捷, 诸葛婴, 等. 图像分割方法综述[J]. 模式识别与人工智能, 1999,12(3):300-312.
	[ Luo X P, Tian J, Zhuge Y, et al. Overview of image segmentation methods[J]. Pattern Recognition and Artificial Intelligence, 1999,12(3):300-312. ]
[13]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc, 2012: 1097-1105.
[14]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2014-09-04]. https://arxiv.org/abs/1712.031497.
[15]	庄福振, 罗平, 何清, 等. 迁移学习研究进展[J]. 软件学报, 2015,26(1):26-39.
	[ Zhuang F Z, Luo P, He Q, et al. Research progress of transfer learning[J]. Journal of Software, 2015,26(1):26-39. ]
[16]	郑远攀, 李广阳, 李晔. 深度学习在图像识别中的应用研究综述[J]. 计算机工程与应用, 2019,55(12):20-36.
	[ Zheng Y P, Li G Y, Li Y. A review of the application of deep learning in image recognition[J]. Computer Engineering and Application, 2019,55(12):20-36. ]
[17]	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2015: 1-9.
[18]	Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2016: 2818-2826.
[19]	李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016,36(9):2508-2515,2565.
	[ Li Y D, Hao Z B, Lei H. Review of convolutional neural network research[J]. Computer Application, 2016,36(9):2508-2515,2565. ]
[20]	王莉影, 高心丹. 基于迁移学习的遥感影像树种类型分类[J]. 实验室研究与探索, 2019,38(9):29-33,78.
	[ Wang L Y, Gao X D. Classification of tree species in remote sensing image based on migration learning[J]. Laboratory Research and Exploration, 2019,38(9):29-33,78. ]
[21]	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017,40(6):1229-1251.
	[ Zhou F Y, Jin L P, Dong J. Review of convolution neural network research[J]. Journal of Computer Science, 2017,40(6):1229-1251. ]

类别	训练集数量	验证集数量	测试集数量	合计
工业用地	797	100	200	1097
林地	763	108	217	1088
耕地	9033	1214	2429	12 676
水体	665	94	189	948
住宅用地	2222	317	634	3173
合计	13 480	1833	3669	18 982

学习方法	模型	验证集精度	测试集精度
迁移学习	VGG16	0.916	0.902
迁移学习	Inception_v3	0.966	0.934

	精确率(P)	召回率(R)	F1	准确率(OA)
工业用地	0.909	0.875	0.892	0.934
林地	0.965	0.925	0.945
耕地	0.929	0.936	0.933
水体	0.899	0.910	0.905
住宅	0.981	0.994	0.987
均值	0.937	0.928	0.932

Land Type Interpretation Authenticity Check of Vector Patch Supported by Deep Learning and Remote Sensing Image

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 18

References 21

Related Articles 15

Metrics

Comments

Recommended 0

[1]	CHEN Ang, YANG Xiuchun, XU Bin, JIN Yunxiang, ZHANG Wenbo, GUO Jian, XING Xiaoyu, YANG Dong. Research on Recognition Methods of Elm Sparse Forest based on Object-based Image Analysis and Deep Learning [J]. Journal of Geo-information Science, 2020, 22(9): 1897-1909.
[2]	CUI Cheng, REN Hongyan, ZHAO Lu, ZHUANG Dafang. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery [J]. Journal of Geo-information Science, 2020, 22(6): 1330-1338.
[3]	MAO Wenshan, ZHAO Hongli, SUN Fengjiao, JIANG Yunzhong, JIANG Qian, ZHU Yanru. Personalized Recommendation Method of Thematic Map Products based on Item2Vec with Negative Sampling Optimization [J]. Journal of Geo-information Science, 2020, 22(11): 2128-2139.
[4]	SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia. A Tentative Study on System of Software Technology for Artificial Intelligence GIS [J]. Journal of Geo-information Science, 2020, 22(1): 76-87.
[5]	CAI Bowen,WANG Shugen,WANG Lei,SHAO Zhenfeng. Extraction of Urban Impervious Surface from High-Resolution Remote Sensing Imagery based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(9): 1420-1429.
[6]	LI Zeyu,MING Dongping,FAN Yinglin,ZHAO Linfeng,LIU Simin. Comparison of Evaluation Indexes for Supervised Segmentation of Remote Sensing Imagery [J]. Journal of Geo-information Science, 2019, 21(8): 1265-1274.
[7]	Huihui CAI, Wei ZHU, Zixuan LI, Yuanyuan LIU, Longbin LI, Chang LIU. Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(6): 928-936.
[8]	Ying FANG, Lianfa LI. Spatiotemporal Estimation of High-Accuracy and High-Resolution Meteorological Parameters based on Machine Learning [J]. Journal of Geo-information Science, 2019, 21(6): 799-813.
[9]	Gang ZHANG, Wenbin LIU, Nan ZHANG. Progressive Morphological Filtering Method of Dense Matching Point Cloud based on Region Feature Segmentation [J]. Journal of Geo-information Science, 2019, 21(4): 615-622.
[10]	Yao YAO, Shuliang REN, Junyi WANG, Qingfeng GUAN. Mapping the Fine-Scale Housing Price Distribution by Integrating a Convolutional Neural Network and Random Forest [J]. Journal of Geo-information Science, 2019, 21(2): 168-177.
[11]	Yilan LIU, Xiaoxia HUANG, Hongga LI, Ze LIU, Chong CHENG, Xin'ge WANG. Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method [J]. Journal of Geo-information Science, 2019, 21(2): 259-268.
[12]	LIU Hao, LUO Jiancheng, HUANG Bo, YANG Haiping, HU Xiaodong, XU Nan, XIA Liegang. Building Extraction based on SE-Unet [J]. Journal of Geo-information Science, 2019, 21(11): 1779-1789.
[13]	Liying ZHANG, Tao PEI, Yijin CHEN, Ci SONG, Xiaoqian LIU. A Review of Urban Environmental Assessment based on Street View Images [J]. Journal of Geo-information Science, 2019, 21(1): 46-58.
[14]	LUO Xing,XU Weiming,WANG Jia. Change Detection Method for High Resolution Remote Sensing Images Based on BOW Features Representation [J]. Journal of Geo-information Science, 2018, 20(8): 1150-1159.
[15]	WANG Chengjun,MAO Zhengyuan,XU Weiming,WENG Qian. Change Detection Approach for High Resolution Remotely Sensed Images Based on Superpixel and Active Learning [J]. Journal of Geo-information Science, 2018, 20(2): 235-245.