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纸质出版日期： 2021-07-07 ，
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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
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. National Remote Sensing Bulletin， 25（7）：1351-1373
通过对地观测可在影像空间上全面反映地表地理现象、格局及演化过程，基于遥感数据提取的土地利用与覆盖变化（LUCC）产品为分析地理要素空间分布规律及其变化机制提供全覆盖、定量化和快速更新的本底信息。本文以高分辨率遥感视觉特征（图）与多源多模态观测机理特征（谱）相互耦合的图谱认知理论研究为基础，提出了精细地理图斑空间结构（精）上融合定量指标反演模型（准）的精准LUCC简称P-LUCC（Precision LUCC）的概念，并依据“五土合一”的地理学思想构建了分层感知、时空协同与多粒度决策于一体的P-LUCC遥感信息智能提取与综合地理应用方法，提出并解析了其中视觉感知的深度学习、外部知识逐步融入的迁移学习以及增量式的自组织强化学习等3类机器学习模型的协同计算机制。基于此，进一步设计了“分区控制—分层提取—分级迁移—功能重组”为途径的高分辨率遥感P-LUCC信息产品生产线系统，并以苏州市高新区为应用区域针对土地利用（LU）图斑的形态、类型以及土地覆盖变化（LCC）的指标等内容开展了P-LUCC产品精度、生产效率的综合分析，展望了基于P-LUCC信息产品开展专题应用研究的新思路。
Through the earth observation
the geographical phenomena
patterns
and evolutionary processes on the earth’s surface can be fully reflected by the earth observation of remotely sensed imageries. The Land Use/Cover Change (LUCC) products from the High-resolution Remote Sensing (HSRS) data can provide full coverage
quantitative
and fast-updating background information for analyzing the law of spatial distribution of geographic features and their changing mechanisms. The development of HSRS is deeply driving the accuracy of LUCC information. At present
from the implementation effect of major survey projects such as national geographic conditions survey and natural resources survey deployed at the national level
the industrial production of LUCC information can be realized through the combination of human-computer interaction
in-house interpretation
and field survey. The development of high-resolution remote sensing is deeply driving the accuracy of LUCC information. How to improve the intelligent productivity of LUCC products through the innovation of theory and technology is an important bottleneck and key challenge.
In the previous research
we put forward the basic concept of geo-parcel/geo-object based on the experience and mode of LUCC production in the departments of land investigation
and determined that the high-precision-level LUCC production is a gradually deepening cognitive process from external visual understanding to internal mechanism analysis. Therefore
this paper takes the geo-parcel as the basic unit of the cognition of land information of earth's surface
and further clarifies the meaning and geographical characteristics of LUCC.
Based on the above background
we proposed a new concept of Precise LUCC (P-LUCC) in this paper
which integrates (accurate) quantitative index inversion model on the spatial structure of (fine) geo-parcels. First
this concept is a derivative of our developed spatial-spectrum cognitive theory
which is achieved by coupling HSRS visual features (TU-spatial maps) with multi-source and multi-modal observation mechanism features (PU-spectrum). Moreover
based on the geographical idea of “the unity of five land features”
we further proposed a series of intelligent remote sensing information extraction methods for P-LUCC production. They are organized hierarchically in three kinds of models
i.e. stratified perception model
spatiotemporal synergistically inversion model
and multi-granular decision-making model. In this aspect
we analyzed the cooperative computing mechanism of three types of machine learning models
namely deep learning for visual perception
transfer learning of external knowledge integration
and reinforcement learning via incremental self-organizing. Multi-type learning algorithms based on these mechanism are organized and transformed organically by using the route of “zoning partition-stratified extraction-graded transfer-functional reconstruction”. Thus
we designed a P-LUCC product production line for an information system of HSRS intelligent interpretation.
Experiments were performed in the Suzhou High-tech Zone
China
and the accuracy and production efficiency of P-LUCC products were analyzed comprehensively. Through this large regional experimental verification
we show that our proposed technology has obvious advantages in the production accuracy and efficiency of LUCC products. Finally
we also provide some new ideas on thematic application based on P-LUCC information products. In conclusion
this intelligent production mode is worthy of popularization and application in engineering natural resources survey.
地理图斑/图谱精准LUCC（P-LUCC）五土合一机器学习智能计算空间优化
geo-parcel/TUPUprecision LUCCthe unity of five land featuresmachine learningintelligent computingspatial optimization
Andrés S, Arvor D and Pierkot C. 2012. Towards an ontological approach for classifying remote sensing images//Proceedings of 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems. Sorrento, Italy: IEEE: 825-832 [DOI: 10.1109/SITIS.2012.124http://dx.doi.org/10.1109/SITIS.2012.124]
Bartholomé E and Belward A S. 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9): 1959-1977 [DOI: 10.1080/01431160412331291297http://dx.doi.org/10.1080/01431160412331291297]
Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R Q, van der Meer F, van der Werff H, van Coillie F and Tiede D. 2014. Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180-191 [DOI: 10.1016/j.isprsjprs.2013.09.014http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014]
Blaschke T, Hay G J, Weng Q H and Resch B. 2011. Collective sensing: integrating geospatial technologies to understand urban systems—an overview. Remote Sensing, 3(8): 1743-1776 [DOI: 10.3390/rs3081743http://dx.doi.org/10.3390/rs3081743]
Cai Y L. 2001. A study on land use/cover change: the need for a new integrated approach. Geographical Research, 20(6): 645-652
蔡运龙. 2001. 土地利用/土地覆被变化研究: 寻求新的综合途径. 地理研究, 20(6): 645-652 [DOI: 10.3321/j.issn:1000-0585.2001.06.001http://dx.doi.org/10.3321/j.issn:1000-0585.2001.06.001]
Chen J and Chen J. 2018. GlobeLand30: operational global land cover mapping and big-data analysis. Science China Earth Sciences, 61(10): 1533-1534
陈军, 陈晋. 2018. GlobeLand30遥感制图创新与大数据分析. 中国科学: 地球科学, 48(10): 1391-1392 [DOI: 10.1360/N072018-00117http://dx.doi.org/10.1360/N072018-00117]
Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, Peng S, Han G, Zhang H W, He C Y, Wu H and Lu M. 2014. Concepts and key techniques for 30 m global land cover mapping. Acta Geodaetica et Cartographica Sinica, 43(6): 551-557
陈军, 陈晋, 廖安平, 曹鑫, 陈利军, 陈学泓, 彭舒, 韩刚, 张宏伟, 何超英, 武昊, 陆苗. 2014. 全球30m地表覆盖遥感制图的总体技术. 测绘学报, 43(6): 551-557 [DOI: 10.13485/jc.nki1.1-20892.0140.089http://dx.doi.org/10.13485/jc.nki1.1-20892.0140.089]
Chen Y Q and Yang P. 2001. Recent progresses of international study on land use and land cover change(LUCC). Economic Geography, 21(1): 95-100
陈佑启, 杨鹏. 2001. 国际上土地利用/土地覆盖变化研究的新进展. 经济地理, 21(1): 95-100 [DOI: 10.3969/j.issn.1000-8462.2001.01.022http://dx.doi.org/10.3969/j.issn.1000-8462.2001.01.022]
Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107 [DOI: 10.1109/JSTARS.2014.2329330http://dx.doi.org/10.1109/JSTARS.2014.2329330]
Demir B, Bovolo F and Bruzzone L. 2012. Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 300-312 [DOI: 10.1109/TGRS.2012.2195727http://dx.doi.org/10.1109/TGRS.2012.2195727]
Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote sensing of Environment, 114(1): 168-182 [DOI: 10.1016/j.rse.2009.08.016http://dx.doi.org/10.1016/j.rse.2009.08.016]
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]
Fu B J and Liu Y X. 2019. The theories and methods for systematically understanding land resource. Chinese Science Bulletin, 64(21): 2172-2179
傅伯杰, 刘炎序. 2019. 系统认知土地资源的理论与方法. 科学通报, 64(21): 2172-2179 [DOI: 10.1360/N972018-01149http://dx.doi.org/10.1360/N972018-01149]
Fu G, Liu C J, Zhou R, Sun T and Zhang Q J. 2017. Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing, 9(5): 498 [DOI: 10.3390/rs9050498http://dx.doi.org/10.3390/rs9050498]
Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]
Hansen M C, Defries R S, Townshend J R G and Sohlberg R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6/7): 1331-1364 [DOI: 10.1080/014311600210209http://dx.doi.org/10.1080/014311600210209]
Huang B, Zhao B and Song Y M. 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214: 73-86 [DOI: 10.1016/j.rse.2018.04.050http://dx.doi.org/10.1016/j.rse.2018.04.050]
Iounousse J, Er-Raki S, Motassadeq A E and Chehouani H. 2015. Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images. Applied Soft Computing, 30: 1-13 [DOI: 10.1016/j.asoc.2015.01.037http://dx.doi.org/10.1016/j.asoc.2015.01.037]
Jean N, Burke M, Xie M, Davis W M, Lobell D B and Ermon S. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301): 790-794 [DOI: 10.1126/science.aaf7894http://dx.doi.org/10.1126/science.aaf7894]
LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444 [DOI: 10.1038/nature14539http://dx.doi.org/10.1038/nature14539]
Li J, Bioucas-Dias J M and Plaza A. 2013. Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Transactions on Geoscience and Remote Sensing, 51(2): 844-856 [DOI: 10.1109/TGRS.2012.2205263http://dx.doi.org/10.1109/TGRS.2012.2205263]
Li X B. 1996. A review of the international researches on land use/land cover change. Acta Geographica Sinica, 51(6): 553-558
李秀彬. 1996. 全球环境变化研究的核心领域──土地利用/土地覆被变化的国际研究动向. 地理学报, 51(6): 553-558[DOI: 10.11821/xb199606009http://dx.doi.org/10.11821/xb199606009]
Liu J Y, Kuang W H, Zhang Z X, Xu X L, Qin Y W, Ning J, Zhou W C, Zhang S W, Li R D, Yan C Z, Wu S X, Shi X Z, Jiang N, Yu D S, Pan X Z and Chi W F. 2014. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica, 69(1): 3-14
刘纪远, 匡文慧, 张增祥, 徐新良, 秦元伟, 宁佳, 周万村, 张树文, 李仁东, 颜长珍, 吴世新, 史学正, 江南, 于东升, 潘贤章, 迟文峰. 2014. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局. 地理学报, 69(1): 3-14 [DOI: 10.11821/dlxb201401001http://dx.doi.org/10.11821/dlxb201401001]
Liu J Y, Liu M L, Zhuang D F, Zhang Z X and Deng X Z. 2002. Study on spatial pattern of land-use change in china during 1995—2000. Science in China (Series D), 32(12): 1031-1040
刘纪远, 刘明亮, 庄大方, 张增祥, 邓祥征. 2002. 中国近期土地利用变化的空间格局分析. 中国科学(D辑), 32(12): 1031-1040 [DOI: 10.3969/j.issn.1674-7240.2002.12.008http://dx.doi.org/10.3969/j.issn.1674-7240.2002.12.008]
Liu J Y, Zhang Z X, Xu X L, Kuang W H, Zhou W C, Zhang S W, Li R D, Yan C Z, Yu D S, Wu S X and Jiang N. 2009. Spatial patterns and driving forces of land use change in China in the early 21st century. Acta Geographica Sinica, 64(12): 1411-1420
刘纪远, 张增祥, 徐新良, 匡文慧, 周万村, 张树文, 李仁东, 颜长珍, 于东升, 吴世新, 江南. 2009. 21世纪初中国土地利用变化的空间格局与驱动力分析. 地理学报, 64(12): 1411-1420 [DOI: 10.3321/j.issn:0375-5444.2009.12.001http://dx.doi.org/10.3321/j.issn:0375-5444.2009.12.001]
Liu X W, Chen B M and Shi X Z. 2004. A review of the research on land use and land cover change in china. Soils, 36(2): 132-135, 140
刘新卫, 陈百明, 史学正. 2004. 国内LUCC研究进展综述. 土壤, 36(2): 132-135, 140 [DOI: 10.3321/j.issn:0253-9829.2004.02.004http://dx.doi.org/10.3321/j.issn:0253-9829.2004.02.004]
Liu Y L and Li X. 2014. Domain adaptation for land use classification: a spatio-temporal knowledge reusing method. Journal of Photogrammetry and Remote Sensing, 98: 133-144 [DOI: 10.1016/j.isprsjprs.2014.09.013http://dx.doi.org/10.1016/j.isprsjprs.2014.09.013]
Loveland T R, Reed B C, Brown J F, Ohlen D O, Zhu Z, Yang L and Merchant J W. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6/7): 1303-1330 [DOI: 10.1080/014311600210191http://dx.doi.org/10.1080/014311600210191]
Luo J C, Hu X D, Wu W and Wang B. 2016a. Collaborative computing technology of geographical big data. Journal of Geo-Information Science, 18(5): 590-598
骆剑承, 胡晓东, 吴炜, 王博. 2016a. 地理时空大数据协同计算技术. 地球信息科学学报, 18(5): 590-598 [DOI: 10.3724/SP.J.1047.2016.00590http://dx.doi.org/10.3724/SP.J.1047.2016.00590]
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, 22(1): 57-75 [DOI: 10.12082/dqxxkx.2020.190462http://dx.doi.org/10.12082/dqxxkx.2020.190462]
Luo J C, Wu T J and Xia L G. 2016b. The theory and calculation of spatial-spectral cognition of remote sensing. Journal of Geo-Information Science, 18(5): 578-589
骆剑承, 吴田军, 夏列钢. 2016b. 遥感图谱认知理论与计算. 地球信息科学学报, 18(5): 578-589 [DOI: 10.3724/SP.J.1047.2016.00578http://dx.doi.org/10.3724/SP.J.1047.2016.00578]
Ma L, Liu Y, Zhang X L, Ye Y X, Yin G F and Johnson B A. 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 166-177 [DOI: 10.1016/j.isprsjprs.2019.04.015http://dx.doi.org/10.1016/j.isprsjprs.2019.04.015]
Marmanis D, Datcu M, Esch T and Stilla U. 2016. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13(1): 105-109 [DOI: 10.1109/LGRS.2015.2499239http://dx.doi.org/10.1109/LGRS.2015.2499239]
Marsetič A, Oštir K and Fras M K. 2015. Automatic orthorectification of high-resolution optical satellite images using vector roads. IEEE Transactions on Geoscience and Remote Sensing, 53(11): 6035-6047 [DOI: 10.1109/TGRS.2015.2431434http://dx.doi.org/10.1109/TGRS.2015.2431434]
Mou L C, Ghamisi P and Zhu X X. 2017. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3639-3655 [DOI: 10.1109/TGRS.2016.2636241http://dx.doi.org/10.1109/TGRS.2016.2636241]
Myint S W, Gober P, Brazel A, Grossman-Clarke S and Weng Q H. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5): 1145-1161 [DOI: 10.1016/j.rse.2010.12.017http://dx.doi.org/10.1016/j.rse.2010.12.017]
Qian X S, Yu J Y and Dai R W. 1993. A new discipline of science - The study of open complex giant system and its methodology. Journal of Systems Engineering and Electronics, 4(2): 2-12
Shen Y C. 1992. On land resource structure and its function—a case study of the arid area in Ningxia and Gansu provinces. Acta Geographica Sinica, 47(6): 489-498
申元村. 1992. 土地资源结构及其功能的研究——以宁夏、甘肃干旱区为例. 地理学报, 47(6): 489-498[DOI: 10.11821/xb199206002http://dx.doi.org/10.11821/xb199206002]
Stumpf A, Lachiche N, Malet J P, Kerle N and Puissant A. 2014. Active learning in the spatial domain for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2492-2507 [DOI: 10.1109/TGRS.2013.2262052http://dx.doi.org/10.1109/TGRS.2013.2262052]
Tuia D, Pasolli E and Emery W J. 2011. Using active learning to adapt remote sensing image classifiers. Remote Sensing of Environment, 115(9): 2232-2242 [DOI: 10.1016/j.rse.2011.04.022http://dx.doi.org/10.1016/j.rse.2011.04.022]
UCL (Université catholique de Louvain), ESA (European Space Agency). 2011. GLOBCOVER 2009 Products Description and Validation Report.(Https：//www. docin. com/P-412963669.html) [2019-10-31]
Wickham J D, Stehman S V, Fry J A, Smith J H and Homer C G. 2010. Thematic accuracy of the NLCD 2001 land cover for the conterminous United States. Remote Sensing of Environment, 114(6): 1286-1296 [DOI: 10.1016/j.rse.2010.01.018http://dx.doi.org/10.1016/j.rse.2010.01.018]
Wu B F, Yuan Q Z, Yan C Z, Wang Z M, Yu X F, Li A N, Ma R H, Huang J L, Chen J S, Chang C, Liu C L, Zhang L, Li X S, Zeng Y and Bao A M. 2014. Land cover changes of china from 2000 to 2010. Quaternary Sciences, 34(4): 723-731
吴炳方, 苑全治, 颜长珍, 王宗明, 于信芳, 李爱农, 马荣华, 黄进良, 陈劲松, 常存, 刘成林, 张磊, 李晓松, 曾源, 包安明. 2014. 21世纪前十年的中国土地覆盖变化. 第四纪研究, 34(4): 723-731 [DOI: 10.3969/j.issn.1001-7410.2014.04.04http://dx.doi.org/10.3969/j.issn.1001-7410.2014.04.04]
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 W Z and Leung Y. 2011. Theory and applications of granular labelled partitions in multi-scale decision tables. Information Sciences, 181(18): 3878-3897 [DOI: 10.1016/j.ins.2011.04.047http://dx.doi.org/10.1016/j.ins.2011.04.047]
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]
Wulder M A, Coops N C, Roy D P, White J C and Hermosilla T. 2018. Land cover 2.0. International Journal of Remote Sensing, 39(12): 4254-4284 [DOI: 10.1080/01431161.2018.1452075http://dx.doi.org/10.1080/01431161.2018.1452075]
Yang Y P, Huang Q T, Wu W, Luo J C, Gao L J, Dong W, Wu T J and Hu X D. 2017. Geo-parcel based crop identification by integrating high spatial-temporal resolution imagery from multi-source satellite data. Remote Sensing, 9(12): 1298 [DOI: 10.3390/rs9121298http://dx.doi.org/10.3390/rs9121298]
Zadeh L A. 1979. Fuzzy sets and information granularity//Gupta N, Ragade R, Yager R, eds. Advances in Fuzzy Set Theory and Applications. Amsterdam: World Scientific Publishing: 111-127
Zhang J H, Feng Z M and Jiang L G. 2011. Progress on studies of land use/land cover classification systems. Resources Science, 33(6): 1195-1203
张景华, 封志明, 姜鲁光. 2011. 土地利用/土地覆被分类系统研究进展. 资源科学, 33(6): 1195-1203
Zhang L P, Zhang L F and Du B. 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2): 22-40 [DOI: 10.1109/MGRS.2016.2540798http://dx.doi.org/10.1109/MGRS.2016.2540798]
Zhang Z X, Wang X, Wen Q K, Zhao X L, Liu F, Zuo L J, Hu S G, Xu J Y, Yi L and Liu B. 2016. Research progress of remote sensing application in land resources. Journal of Remote Sensing, 20(5): 1243-1258
张增祥, 汪潇, 温庆可, 赵晓丽, 刘芳, 左丽君, 胡顺光, 徐进勇, 易玲, 刘斌. 2016. 土地资源遥感应用研究进展. 遥感学报, 20(5): 1243-1258 [DOI: 10.11834/jrs.20166149http://dx.doi.org/10.11834/jrs.20166149]
Zhou C H and Luo J C. 2009. Geoscience Computing of High Resolution Satellite Remote Sensing Image. Beijing: Science and Technology Press: 174
周成虎, 骆剑承. 2009. 高分辨率卫星遥感影像地学计算. 北京: 科学出版社: 174
Zhu L, Chen Y S, Ghamisi P and Benediktsson J A. 2018. Generative adversarial networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5046-5063 [DOI: 10.1109/TGRS.2018.2805286http://dx.doi.org/10.1109/TGRS.2018.2805286]
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