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吴田军，骆剑承，张新，董文，黄启厅，周亚男，刘巍，孙营伟，杨颖频，胡晓东，郜丽静.XXXX.基于地理图斑的遥感粒计算与精准应用.遥感学报，XX（XX）： 1-21
WU Tianjun，LUO Jiancheng，ZHANG Xin，DONG Wen，Huang Qiting，ZHOU Ya’nan，LIU Wei，SUN Yinwei，YANG Yinping，HU Xiaodong，GAO Lijing. XXXX. Remote sensing granular computing and precise applications based on geo-parcels. National Remote Sensing Bulletin， XX（XX）：1-21
当今遥感技术实现了对地球表面进行全覆盖影像记录（空间）、快速信息更新（时间）、多手段协同观测（属性）的大数据获取，随之由遥感数据向地理信息与知识转换的鸿沟问题日益凸显。以数据粒化为基础的粒计算是大数据处理领域模拟人类思考和解决大规模复杂问题的前沿方向，其通过结构化、关联化等手段提升模式挖掘与知识发现的精度与效率。本文遵照从“外在场景的视觉理解”到“内在机理的知识发现”的演进脉络，在空间、时间、属性三个维度上剖析了遥感大数据的粒结构及其多层次、多粒度特征，并以“地理图斑”为主线发展了集成“分区分层感知、时空协同反演、多粒度决策”三个基础模型的遥感粒计算方法。面向精准农业应用的案例从多个视角阐释了粒计算契合遥感大数据智能计算的需要，验证了本文构建的理论与方法可对农业遥感多层次的复杂问题实现有序解构与逐步求解，彰显了其助益于领域化精准应用的潜在能力。
Objective Currently, remote sensing technology has realized the acquisition of big data for full coverage image recording (space), rapid information updating (time) and multiple measure collaborative observation (attribute) on the earth surface. Meanwhile, the gap between remote sensing data and geographic information and knowledge is becoming increasingly prominent. Granular computing with data granulation as the basic is a frontier direction in the field of big data processing, which simulates human thinking and solves large-scale complex problems. It helps to improve the accuracy and efficiency of pattern mining and knowledge discovery by means of structure and association. Therefore, it is necessary to consider introducing this kind of data analysis method into the process of information mining and knowledge discovery of remote sensing big data.Method According to the evolution route from "visual understanding of external scene" to "relationship perspective of internal generation mechanism (spectrum analysis)", this paper analyzes the granular structure of remote sensing big data and its multi-level and multi-granularity characteristics from three dimensions of space, time and attribute. Based on the characteristics of remote sensing data, we further determine the corresponding granulation strategy. In addition, we built a methodology of remote sensing granular computing based on geo-parcels, which integrates the basic models of "zonal-stratified perception, spatiotemporal collaborative inversion, and multi-granularity decision making". They integrate geographical analysis methods, remote sensing mechanism models and artificial intelligence algorithms, and mine geographic information or knowledge including morphology, type, index, state, development trend and and mechanism of land geo-parcels.Result The case for precision agriculture application shows that granular computing meets the needs of remote sensing big data intelligent computing from multiple perspectives. It is verified that the theory and method proposed in this paper can realize orderly deconstruction and step-by-step solution for the multi-level complex problems of agricultural remote sensing. The case study also demonstrates its potential ability to help domain precision application.Conclusion This paper develops a remote sensing intelligent computing methodology under the guidance of granular computing, and analyzes the corresponding problems and solutions in the aspects of space, time and attribute. Based on the above work, we believe the proposed the methodology of intelligent interpretation of remote sensing based on granular computing is expected to effectively decompose and solve the complex surface cognitive problems via the earth observation by remote sensing.
遥感大数据粒结构/粒计算地理图斑分区分层感知时空协同反演多粒度决策精准农业应用
remote sensing big datagranular structure/granular computinggeo-parcelzonal-stratified perceptionspatiotemporal collaborative inversionmulti-granularity decision makingprecision agriculture application
Cai B F and Yu R. 2009. Advance and evaluation in the long time series vegetation trends research based on remote sensing. Journal of Remote Sensing, 13(6) : 1170-1186
蔡博峰, 于嵘. 基于遥感的植被长时序趋势特征研究进展及评价. 遥感学报, 2009, 13(6) : 1170-1186 [DOI: 10.3321/j.issn:1007-4619.2009.06.014http://dx.doi.org/10.3321/j.issn:1007-4619.2009.06.014]
Dong W, Wu T J, Luo J C, Sun Y W and Xia L G. 2019. Land-parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China. Geoderma, 340: 234-248 [DOI: 10.1016/j.geoderma.2019.01.018http://dx.doi.org/10.1016/j.geoderma.2019.01.018]
FU B J. 2017. Geography: From knowledge, science to decision making support. Acta Geographica Sinica, 72(11): 1923-1932
傅伯杰. 2017. 地理学: 从知识、科学到决策. 地理学报, 72(11): 1923-1932 [DOI: 10.11821/dlxb201711001http://dx.doi.org/10.11821/dlxb201711001]
Geng L Y and Ma M G. 2014. Advance in Method Comparison of Reconstructing Remote Sensing Time Series Data Sets. Remote Sensing Technology and Application, 29(2): 362-368
耿丽英, 马明国. 2014. 长时间序列NDVI数据重建方法比较研究进展. 遥感技术与应用, 29(2): 362-368 [DOI: 10.11873/j.issn.1004-0323.2014.2.0362http://dx.doi.org/10.11873/j.issn.1004-0323.2014.2.0362]
Hua Y X. 2016. The core problems and key technologies of pan-spatial information system. Journal of Geomatics Science and Technology, 33(4): 331-335
华一新. 2016. 全空间信息系统的核心问题和关键技术. 测绘科学技术学报, 33(4): 331-335 [DOI: 10.3969/j.issn.1673-6338.2016.04.001http://dx.doi.org/10.3969/j.issn.1673-6338.2016.04.001]
Hua Y X and Zhou C H. 2017. Description Frame of Data Model of Multi-granularity Spatio-temporal Object for Pan-spatial Information System. Journal of Geo-Information Science, 19(9): 1142-1149
华一新, 周成虎. 2017. 面向全空间信息系统的多粒度时空对象数据模型描述框架. 地球信息科学学报, 19(9): 1142-1149 [DOI: 10.3724/SP.J.1047.2017.01142http://dx.doi.org/10.3724/SP.J.1047.2017.01142]
He Z, Xie G X, Lin Y J, Huang Q T and Yang S E. 2020. Summary of the application of remote sensing in Chinese sugarcane industry. Land and Resources Informatization, 4, 22-27
何忠, 谢国雪, 林垚君, 黄启厅, 杨绍锷. 2020. 遥感技术在我国甘蔗产业的应用综述. 国土资源信息化, 4, 22-27 [DOI: 10.3969/j.issn.1674-3695.2020.04.004http://dx.doi.org/10.3969/j.issn.1674-3695.2020.04.004]
Jiang N, Fang C and Chen M J. 2017. Initial Exploration of Pan-spatial Cognition and Representation. Journal of Geo-Information Science, 19(9): 1150-1157
江南, 方成, 陈敏颉. 2017. 全空间信息系统认知与表达初探. 地球信息科学学报, 19(9): 1150-1157 [DOI: 10.3724/SP.J.1047.2017.01150http://dx.doi.org/10.3724/SP.J.1047.2017.01150]
Li D R. 2012. On space-air-ground integrated earth observation network. Journal of Geo-Information Science, 14(4): 419-425
李德仁. 2012. 论空天地一体化对地观测网络. 地球信息科学学报, 14(4): 419-425 [DOI: 10.3724/SP.J.1047.2012.00419http://dx.doi.org/10.3724/SP.J.1047.2012.00419]
Li D R, Zhang L P and Xia G S. 2014. Automatic Analysis and Mining of Remote Sensing Big Data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
李德仁, 张良培, 夏桂松. 2014. 遥感大数据自动分析与数据挖掘. 测绘学报, 43(12): 1211-1216 [DOI: 10.13485/jc.nki1.1-20892.0140.187http://dx.doi.org/10.13485/jc.nki1.1-20892.0140.187]
Li D R, Wang M, Shen X, and Dong Z P. 2017. From Earth Observation Satellite to Earth Observation Brain. Geomatics and Information Science of Wuhan University, 42(2): 143-149
李德仁, 王密, 沈欣, 董志鹏. 2017. 从对地观测卫星到对地观测脑 武汉大学学报(信息科学版), 2017, 42(2): 143-149 [DOI: 10.13203/j.whugis20160526http://dx.doi.org/10.13203/j.whugis20160526]
Liang J Y, Qian Y H, Li D Y and Hu Q H. 2015. Theory and method of granular computing for big data mining. Science in China (Information Sciences), 45(11): 1355-1369
梁吉业, 钱宇华, 李德玉, 胡清华. 2015. 大数据挖掘的粒计算理论与方法. 中国科学: 信息科学, 45(11): 1355-1369 [DOI: 10.1360/N112015-00092http://dx.doi.org/10.1360/N112015-00092]
Li R, Shi J H, Dong G S and Liu Z H. 2021. Research on expression of multi-granularity spatio-temporal object composition structure. Journal of Geo-information Science, 23(1):113-123
李锐, 石佳豪, 董广胜, 刘朝辉. 2021. 多粒度时空对象组成结构表达研究. 地球信息科学学报, 23(1):113-123 [DOI: 10.12082/dqxxkx.2021.200423http://dx.doi.org/10.12082/dqxxkx.2021.200423]
Liu W, Wu Z F, Luo J C, Sun Y W, Wu T J, Zhou N, Hu X D, Wang L Y and Zhou Z F. 2021. A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deeplearning. Acta Geodaetica et Cartographica Sinica, 50(1): 105-116
刘巍, 吴志峰, 骆剑承, 孙营伟, 吴田军, 周楠, 胡晓东, 王玲玉, 周忠发. 2021. 深度学习支持下的丘陵山区耕地高分辨率遥感信息分区分层提取方法研究. 测绘学报, 50(1): 105-116 [DOI: 10.11947/j.AGCS.2021.20190448http://dx.doi.org/10.11947/j.AGCS.2021.20190448]
Li Y D, Han Z and Tong X H. 2009. Spatio-temporal data model of multi-granularity. Proceedings of the International Forum on information technology and Applications (Part 2
) (李阳东, 韩震, 童小华. 2009. 一种顾及多粒度的时空数据模型. 国际信息技术与应用论坛论文集(下)
Li Y. 2012. Information Granulation Method of Temporal Data and Application. Beijing: Beijing Normal University
李洋. 2012. 时序数据的信息粒化方法及应用研究. 北京:北京师范大学
Luo J C, Wu T J, Wu Z F, Hu X D, Dong W, Gao L J, Huang Q T, et al. 2020a. Intelligent Computing of Remote Sensing Big Data. Beijing: Science Press
骆剑承，吴田军，吴志峰，胡晓东，董文，郜丽静，黄启厅等. 2020a, 遥感大数据智能计算. 北京: 科学出版社
Luo J C, Hu X D, Wu T J, Liu W, Xia L G, Yang H P, Sun Y W, Xu N, Zhang X, Shen Z F and Zhou N. 2021. Research on intelligent calculation model and method of precision land use/cover change information driven by high-resolution remote sensing. Journal of Remote Sensing, 25(7): 1351-1373
骆剑承, 胡晓东, 吴田军, 刘巍, 夏列钢, 杨海平, 孙营伟, 徐楠, 张新, 沈占锋, 周楠. 2021. 高分遥感驱动的精准土地利用与土地覆盖变化信息智能计算模型与方法研究. 遥感学报, 25(7): 1351-1373 [DOI: 10.11834/jrs.20219402http://dx.doi.org/10.11834/jrs.20219402]
Luo J C, Wu T J, Wu Z F, Zhou Y N, Gao L J, Sun Y W, Wu W, Yang Y P, Hu X D, Zhang X and Shen Z F. 2020. Methods of Intelligent Computation and Pattern Mining based on Geo-parcels. Journal of Geo-Information Science, 22(1): 57-75
骆剑承, 吴田军, 吴志峰，周亚男, 郜丽静, 孙营伟, 吴炜, 杨颖频, 胡晓东, 张新, 沈占锋. 2020. 地理图斑智能计算及模式挖掘方法研究. 地球信息科学学报, 22(1): 57-75 [DOI: CNKI:SUN:DQXX.0.2020-01-008http://dx.doi.org/CNKI:SUN:DQXX.0.2020-01-008]
Lu Y L, Liu C W, Li J W, Jiang J W and Xia Y C. 2020. Geospatial cognition method of biomimetic cell structure under the era of big data. Science of Surveying and Mapping, 45(9), 174-179+198
陆妍玲, 刘采玮, 李景文, 姜建武, 夏勇超. 2020. 大数据时代细胞仿生结构的地理空间认知方法. 测绘科学, 45(9), 174-179+198 [DOI: 10.16251/j.cnki.1009-2307.2020.09.026http://dx.doi.org/10.16251/j.cnki.1009-2307.2020.09.026]
Miao D Q, Zhang Q H, Qian Y H, Liang J Y, Wang G Y, Wu W Z, Gao Y, Shang L, Gu S M and Zhang H Y. 2016. From human intelligence to machine implementation model: theories and applications based on granular computing. CAAI Transactions on Intelligent Systems, 11(6): 743-757
苗夺谦, 张清华, 钱宇华, 梁吉业, 王国胤, 吴伟志, 高阳, 商琳, 顾沈明, 张红云. 2016. 从人类智能到机器实现模型——粒计算理论与方法. 智能系统学报, 11(6): 743-757 [DOI: 10.11992/tis.201612014http://dx.doi.org/10.11992/tis.201612014]
Pei T, Liu Y X, Guo S H, Shu H, Du Y Y, Ma T and Zhou C H. 2019. Principle of big geodata mining. Acta Geographica Sinica, 74(3): 586-598.
裴韬, 刘亚溪, 郭思慧, 舒华, 杜云艳,马廷, 周成虎. 2019. 地理大数据挖掘的本质. 地理学报, 74(3): 586-598 [DOI: 10.11821/dlxb201903014http://dx.doi.org/10.11821/dlxb201903014]
Tong X D. 2016. Development of China high-resolution earth observation system. Journal of Remote Sensing, 20(5): 775-780
童旭东. 2016. 中国高分辨率对地观测系统重大专项建设进展. 遥感学报, 2016, 20(5): 775-780 [DOI: 10.11834/jrs.20166302http://dx.doi.org/10.11834/jrs.20166302]
Wang G Y, Li S, Yang J. 2018. A multi-granularity cognitive computing model bidirectionally driven by knowledge and data. Journal of Northwest University (Natural Science Edition), 48(4): 481-500
王国胤, 李帅, 杨洁. 2018. 知识与数据双向驱动的多粒度认知计算.西北大学学报(自然科学版), 48(4): 481-500 [DOI: 10.16152/j.cnki.xdxbzr.2018-04-002http://dx.doi.org/10.16152/j.cnki.xdxbzr.2018-04-002]
Wu T J, Dong W, Luo J C, Sun Y W, Huang Q T, Wu W Z and Hu X D. 2019. Geo-parcel-based geographical thematic mapping using C5.0 decision tree: A case study of evaluating sugarcane planting suitability. Earth Science Informatics, 12(1): 57-70 [DOI: 10.1007/s12145-018-0360-8http://dx.doi.org/10.1007/s12145-018-0360-8]
Wu T J, Luo J C, Gao L J, Sun Y W, Yang Y P, Zhou Y N, Dong W and Zhang X. 2021. A geo-parcel-based spatial prediction method for grassland fractional vegetation cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, to be published [DOI: 10.1109/JSTARS.2021.3110896http://dx.doi.org/10.1109/JSTARS.2021.3110896]
Wu W Z, Leung Y and Mi J S. 2009. Granular computing and knowledge reduction in formal contexts. IEEE Transactions on Knowledge and Data Engineering, 21(10): 1461-1474 [DOI: 10.1109/TKDE.2008.223http://dx.doi.org/10.1109/TKDE.2008.223]
Wu Z F, Luo J C, Sun Y W, Wu T J, Cao Z, Liu W, Yang Y P and Wang L Y. 2020. Research on Precision Agricultural based on the Spatial-temporal Remote Sensing Collaboration. Journal of Geo-Information Science, 22(4): 731-742
吴志峰, 骆剑承, 孙营伟, 吴田军, 曹峥, 刘巍, 杨颖频, 王玲玉. 2020. 时空协同的精准农业遥感研究. 地球信息科学学报, 22(4): 731-742. [DOI: CNKI:SUN:DQXX.0.2020-04-011http://dx.doi.org/CNKI:SUN:DQXX.0.2020-04-011]
Xie G X, Huang Q T, Zeng Z K, Qin Z L, Zhang X L and Su Q Q. 2018. Precision Planning of SUGARCANE INDUSTRY IN SCALE OF HILLY AREA. Chinese Journal of Agricultural Resources and Regional Planning, 39(5): 154-163
谢国雪, 黄启厅, 曾志康, 覃泽林, 张秀龙, 苏秋群. 2018. 丘陵县域地块尺度的甘蔗产业精准规划研究. 中国农业资源与区划, 39(5): 154-163 [DOI: 10.7621/cjarrp.1005-9121.20180521http://dx.doi.org/10.7621/cjarrp.1005-9121.20180521]
Xu J, Wang G Y, Yu H. 2014. Review of Big Data Processing Based on Granular Computing. Chinese Journal of Computers, 37(11): 1-22
徐计, 王国胤, 于洪. 2014. 基于粒计算的大数据处理. 计算机学报, 37(11): 1-22 [DOI: 10.11897/SP.J.1016.2015.01497http://dx.doi.org/10.11897/SP.J.1016.2015.01497]
Xu N, Luo J C, Zuo J, Hu X D, Dong J, Wu T J, Wu S L and Liu Hao. 2020. Accurate mapping for the suitability evaluation of large-scale roof greening based on RS and GIS methods. Sustainability, 12(11), 1-23 [DOI: 10.3390/su12114375http://dx.doi.org/10.3390/su12114375]
Xu Z B and Sun J. 2018. Model-driven deep learning. National Science Review, 1(5): 22-24 [DOI: 10.1093/nsr/nwx099http://dx.doi.org/10.1093/nsr/nwx099]
Yang Y P, Wu Z F, Luo J C, Huang Q T, Zhang D Y, Wu T J, Sun Y W, Cao Z, Dong W and Liu W. 2021. Parcel-based crop distribution extraction using the spatiotemporal collaboration of remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 37(7): 166-174
杨颖频, 吴志峰, 骆剑承, 黄启厅, 张冬韵, 吴田军, 孙营伟, 曹峥, 董文, 刘巍. 2021. 时空协同的地块尺度作物分布遥感提取研究. 农业工程学报, 37(7): 166-174 [DOI: 10.11975/j.issn.1002-6819.2021.07.020http://dx.doi.org/10.11975/j.issn.1002-6819.2021.07.020]
Zadeh L A. 1997. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90: 111-127 [DOI: 10.1016/S0165-0114(97)00077-8http://dx.doi.org/10.1016/S0165-0114(97)00077-8]
Zeng M X, Hua Y X, Zhang J S, CAO Y B and ZHANG Z. 2021. Research on dynamic behavior expression model and method of multi-granularity spatio-temporal objects. Journal of Geo-information Science, 23(1):104-112
曾梦熊, 华一新, 张江水, 曹一冰, 张政. 2021. 多粒度时空对象动态行为表达模型与方法研究. 地球信息科学学报, 23(1): 104-112. [DOI:10.12082/dqxxkx.2021.200420http://dx.doi.org/10.12082/dqxxkx.2021.200420]
Zheng Y. 2015. Methodologies for cross-domain data fusion: An overview. IEEE Transactions on Big Data, 1(1): TBD-2015-05-0037 [DOI: 10.1109/TBDATA.2015.2465959http://dx.doi.org/10.1109/TBDATA.2015.2465959]
Zhou C H. 2015. Prospects on pan-spatial information system. Progress in Geography, 34(2): 129-131
周成虎. 2015. 全空间地理信息系统展望. 地理科学进展, 2015, 34(2): 129-131 [DOI: 10.11820/dlkxjz.2015.02.001http://dx.doi.org/10.11820/dlkxjz.2015.02.001]
Zhang B. 2018. Remotely Sensed Big Data Era and Intelligent Information Extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
张兵. 2018. 遥感大数据时代与智能信息提取. 武汉大学学报(信息科学版), 43(12): 1861-1871 [DOI: 10.13203/j.whugis20180172http://dx.doi.org/10.13203/j.whugis20180172]
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