人机协同的自然资源要素智能提取方法

doi:10.11947/j.AGCS.2021.20210102

摘要/Abstract

摘要： 开展自然资源统一调查监测评价，准确掌握我国各类自然资源家底和变化情况，是科学编制国土空间规划，逐步实现山水林田湖草的整体保护、系统修复和综合治理，保障国家生态安全的基础支撑。目前，基于遥感影像的自然资源要素提取，主要依赖人机交互的目视解译与外业逐图斑核查的方法，劳动强度大、生产效率低、人为因素多，已不能适应自然资源全要素、全流程、全覆盖一体化调查监测的新要求。本文顺应人工智能协作方法智能化发展新方向，总结分析了深度学习智能提取研究进展及存在问题，以及人机协同发展新方向研究现状，从分析自然资源要素的特点出发，提出了“智能计算后台+智能引擎+人机交互前台”人机协同智能提取技术框架，阐述了需要攻克的关键技术及解决途径，探讨了人机协同智能提取云平台构建思路，以期为自然要素智能提取提供新一代人工智能方法及思路，提升自然资源要素提取的自动化与智能化水平。

关键词: 自然资源调查监测, 自然资源要素, 人机协同, 人工智能, 智能提取

Abstract: Carrying out an integrated survey, monitoring and evaluation of natural resources, accurately understanding the status and changes of various natural resources in China, is the scientific basis for territorial and spatial plans, and gradually realizing the overall protection, restoration, and comprehensive management of landscapes (including mountains, forests, fields, lakes and grasses), ensuring national ecological security. At present, the extraction of natural resource features based on remote sensing images mainly relies on visual interpretation via man-machine interaction and field spot verification. It needs high labor intensity, and production efficiency is low. The results are also highly affected by man-induced factor, which can no longer adapt to the requirements for integrated investigation and monitoring of all features of natural resources. This paper conforms to the emerging direction of the research development with artificial intelligence collaboration. Firstly, this paper reviews the main research progress of deep learning technology and its application in the field of remote sensing image intelligent extraction systematically, and analyzes its limitations, then it reviews the main research status of man-machine collaboration. Afterward, Starting from the characteristics of natural resource features, presents a technical framework of "intelligent background computing+intelligent engine+man-machine interface" for man-machine collaborative intelligent extraction. The key technologies that need to be overcome are described. At last, the idea of creating cloud platform for feature extraction are discussed. This paper aims to provide a new AI method for intelligent extraction and improve the automation and intelligence level of natural resource feature extraction.

Key words: natural resources survey and monitoring, natural resources features, man-machine cooperation, artificial intelligence, intelligent extraction

中图分类号:

P208

张继贤, 李海涛, 顾海燕, 张鹤, 杨懿, 谭相瑞, 李淼, 沈晶. 人机协同的自然资源要素智能提取方法[J]. 测绘学报, 2021, 50(8): 1023-1032.

ZHANG Jixian, LI Haitao, GU Haiyan, ZHANG He, YANG Yi, TAN Xiangrui, LI Miao, SHEN Jing. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1023-1032.

参考文献

[1] 自然资源部. 自然资源调查监测体系构建总体方案[EB/OL].(2020-01-17)[2020-01-20].http://www.gov.cn:8080/zhengce/zhengceku/2020-01/18/content_5470398.htm. Ministry of Natural Resources of the People's Republic of China. Overall plan for the construction of the natural resources investigation and monitoring system[EB/OL]. (2020-01-17)[2020-01-20]. http://www.gov.cn:8080/zhengce/zhengceku/2020-01/18/content_5470398.htm.
[2] 周培诚,程塨,姚西文,等.高分辨率遥感影像解译中的机器学习范式[J].遥感学报,2021,25(1):182-197. ZHOU Peicheng,CHENG Gong,YAO Xiwen,et al. Machine learning paradigms in high-resolution remote sensing image interpretation[J]. Journal of Remote Sensing(Chinese),2021, 25(1):182-197.
[3] LI Jiayi, HUANG Xin, GONG Jianya. Deep neural network for remote-sensing image interpretation:status and perspectives[J]. National Science Review, 2019, 6(6):1082-1086.
[4] 龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报, 2018, 47(6):693-704. GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):693-704.
[5] 贺浩, 王仕成, 杨东方, 等. 基于Encoder-Decoder网络的遥感影像道路提取方法[J]. 测绘学报, 2019, 48(3):330-338. HE Hao, WANG Shicheng, YANG Dongfang, et al. An road extraction method for remote sensing image based on Encoder-Decoder network[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3):330-338.
[6] ZHANG Wei, TANG Ping, CORPETTI T, et al. WTS:a weakly towards strongly supervised learning framework for remote sensing land cover classification using segmentation models[J]. Remote Sensing, 2021, 13(3):394.
[7] YAN Xiongfeng, AI Tinghua, YANG Min, et al. A graph convolutional neural network for classification of building patterns using spatial vector data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150:259-273.
[8] MA D, TANG Ping, ZHAO Lijun. SiftingGAN:generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(7):1046-1050.
[9] OUYANG Song, LI Yansheng. Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery[J]. Remote Sensing, 2020, 13(1):119.
[10] PIRES DE LIMA R, MARFURT K. Convolutional neural network for remote-sensing scene classification:transfer learning analysis[J]. Remote Sensing, 2019, 12(1):86.
[11] CUI Binge, CHEN Xin, LU Yan. Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection[J]. IEEE Access, 2020, 8:116744-116755.
[12] XU Guang, ZHU Xuan, TAPPER N. Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images[J]. International Journal of Remote Sensing, 2020, 41(11):4057-4079.
[13] 范向民, 范俊君, 田丰, 等. 人机交互与人工智能:从交替浮沉到协同共进[J]. 中国科学:信息科学, 2019, 49(3):361-368. FAN Xiangmin, FAN Junjun, TIAN Feng, et al. Human-computer interaction and artificial intelligence:from competition to integration[J]. Scientia Sinica (Informationis), 2019, 49(3):361-368.
[14] 周小程,高冬明.人机协同,"智"在必得[N].解放军报,2019-12-06(11). ZHOU Xiaocheng, GAO Dongming. Human-machine collaboration, "wisdom" must be won[N]. People's Liberation Army Daily, 2019-12-06(11).
[15] 邹德宝,人机协同考验AI产业智慧[N].中国电子报,2020-11-20(6). ZOU Debao, Human-machine collaboration tests the wisdom of the AI industry[N]. China Electronics News, 2020-11-20(6).
[16] LENAT D B, FEIGENBAUM E A. On the thresholds of knowledge[J]. Artificial Intelligence, 1991, 47(1):185-230.
[17] 路甬祥, 陈鹰. 人机一体化系统与技术立论[J]. 机械工程学报, 1994, 30(6):1-9. LU Yongxiang, CHEN Ying. Foundation of the humachine system[J]. Chinese Journal of Mechanical Engineering, 1994, 30(6):1-9.
[18] E-works. Explore industry 4.0[EB/OL].[2019-02-20].https://www.e-works.net.cn/report/industry/industry.html.
[19] OSTP. The national artificial intelligence research and development strategic plan:2019 Update[R].[S.l.]:Select Committee on Artificial Intelligence.
[20] BERTINO E, FINALE D, GINI M,et al. Artificial Intelligence &Cooperation[EB/OL].[2020-05-18]. https://cra.org/ccc/resources/ccc-led-whitepapers/#2020-quadren-nialpapers.
[21] 国务院.《新一代人工智能发展规划》国发[2017]35号.[EB/OL].[2017-07-20]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm. The State Council. New generation artificial intelligence development plan" Guofa (2017) No. 35.[EB/OL].[2017-07-20]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[22] 陈玲, 贾佳, 王海庆. 高分遥感在自然资源调查中的应用综述[J]. 国土资源遥感, 2019, 31(1):1-7. CHEN Ling, JIA Jia, WANG Haiqing. An overview of applying high-resolution remote sensing to natural resources survey[J]. Remote Sensing for Land & Resources, 2019, 31(1):1-7.
[23] GU Haiyan, HAN Yanshun, YANG Yi, et al. An efficient parallel multi-scale segmentation method for remote sensing imagery[J]. Remote Sensing, 2018, 10(4):590.
[24] 葛良胜, 夏锐. 自然资源综合调查业务体系框架[J]. 自然资源学报, 2020, 35(9):2254-2269. GE Liangsheng, XIA Rui. Research on comprehensive investigation work system of natural resources[J]. Journal of Natural Resources, 2020, 35(9):2254-2269.
[25] CHENG Gong, XIE Xingxing, HAN Junwei, et al. Remote sensing image scene classification meets deep learning:challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3735-3756.
[26] WANG Junjue, ZHONG Yanfei, ZHENG Zhuo, et al. RSNet:the search for remote sensing deep neural networks in recognition tasks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3):2520-2534.
[27] LE H V, MAYER S, HENZE N. Deep learning for human-computer interaction[J]. Interactions, 2021, 28(1):78-82.
[28] 汪敏, 刘轩山, 陈祎, 等. 一种基于规则引擎的智能推送方法及系统:CN109597931A[P]. 2019-04-09. WANG Min, LIU Xuanshan, CHEN Yi, et al. An intelligent pushing method and system based on a rule engine:CN109597931A[P]. 2019-04-09.
[29] 田彦平, 陶超, 邹峥嵘, 等. 主动学习与图的半监督相结合的高光谱影像分类[J]. 测绘学报, 2015, 44(8):919-926. TIAN Yanping, TAO Chao, ZOU Zhengrong, et al. Semi-supervised graph-based hyperspectral image classification with active learning[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(8):919-926.
[30] 孙熠, 李培军. 利用主动学习改进遥感图像单类分类:以正类和未标记样本学习方法为例[J]. 北京大学学报(自然科学版), 2020, 56(1):155-163. SUN Yi, LI Peijun. Improving one-class classification of remote sensing data by using active learning:a case study of positive and unlabeled learning[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(1):155-163.[知网]
[31] 朱庆, 付萧. 多模态时空大数据可视分析方法综述[J]. 测绘学报, 2017, 46(10):1672-1677. ZHU Qing, FU Xiao. The review of visual analysis methods of multi-modal spatio-temporal big data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1672-1677.
[32] 王占宏, 白穆, 李宏建. 地理空间大数据服务自然资源调查监测的方向分析[J]. 地理信息世界, 2019, 26(1):1-5. WANG Zhanhong, BAI Mu, LI Hongjian. Direction analysis on service for natural resource investigation and monitoring using geospatial big data[J]. Geomatics World, 2019, 26(1):1-5.
[33] SHI Jinshou, TANG Wenzhe, LI Ning, et al. User cognitive abilities-human computer interaction tasks model[M]//Advances in Intelligent Systems and Computing. Cham:Springer International Publishing, 2021:194-199.
[34] 张继贤, 顾海燕, 鲁学军, 等. 地理国情大数据研究框架[J]. 遥感学报, 2016, 20(5):1017-1026. ZHANG Jixian, GU Haiyan, LU Xuejun, et al. Research framework of geographical conditions and big data[J]. Journal of Remote Sensing, 2016, 20(5):1017-1026.