سابِق|تال

Research on intelligent calculation model and method of precision land use/cover change information driven by high-resolution remote sensing

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：luojc@aircas.ac.cn
  • Introduction：骆剑承，1970年生，男，研究员，研究方向为遥感大数据智能计算与地学分析。E-mail：luojc@aircas.ac.cn
LUO Jiancheng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：mydonne@163.com
  • Introduction：胡晓东，1982年生，男，助理研究员，研究方向为遥感信息智能提取与自适应计算。E-mail：mydonne@163.com
HU Xiaodong1*,  
  • Affiliation：

    School of Science, Chang’an University, Xi’an 710064, China

WU Tianjun3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Wei12,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

XIA Liegang4,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

YANG Haiping4,  
  • Affiliation：

    Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

SUN Yingwei5,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XU Nan12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Xin12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SHEN Zhanfeng1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHOU Nan12

ملخص

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.

مفهوم

geo-parcel/TUPU;precision LUCC;the unity of five land features;machine learning;intelligent computing;spatial optimization

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Cai Y L. 2001. A study on land use/cover change: the need for a new integrated approach. Geographical Research, 20(6): 645-652
  6. 6.
    Chen J and Chen J. 2018. GlobeLand30: operational global land cover mapping and big-data analysis. Science China Earth Sciences, 61(10): 1533-1534
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Fu B J. 2017. Geography: from knowledge, science to decision making support. Acta Geographica Sinica, 72(11): 1923-1932
  13. 13.
    Fu B J and Liu Y X. 2019. The theories and methods for systematically understanding land resource. Chinese Science Bulletin, 64(21): 2172-2179
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444
  21. 21.
    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
  22. 22.
    Li X B. 1996. A review of the international researches on land use/land cover change. Acta Geographica Sinica, 51(6): 553-558
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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]
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    Zhou C H and Luo J C. 2009. Geoscience Computing of High Resolution Satellite Remote Sensing Image. Beijing: Science and Technology Press: 174
  54. 54.
    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

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website