• Journal of Geo-information Science
  • Vol. 22, Issue 1, 57 (2020)
Jiancheng LUO1、1、2、2, Tianjun WU3、3、*, Zhifeng WU4、4, Ya'nan ZHOU5、5, Lijing GAO1、1、2、2, Yingwei SUN1、1、2、2, Wei WU6、6, Yingpin YANG1、1、2、2, Xiaodong HU1、1、2、2, Xin ZHANG1、1、2、2, and Zhanfeng SHEN1、1、2、2
Author Affiliations
  • 1State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 1中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 2中国科学院大学,北京 100049
  • 3School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710064, China
  • 3长安大学 地质工程与测绘学院,西安 710064
  • 4School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • 4广州大学地理科学学院,广州 510006
  • 5School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
  • 5河海大学地球科学与工程学院,南京 211100
  • 6College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • 6浙江工业大学计算机学院,杭州 310023
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    DOI: 10.12082/dqxxkx.2020.190462 Cite this Article
    Jiancheng LUO, Tianjun WU, Zhifeng WU, Ya'nan ZHOU, Lijing GAO, Yingwei SUN, Wei WU, Yingpin YANG, Xiaodong HU, Xin ZHANG, Zhanfeng SHEN. Methods of Intelligent Computation and Pattern Mining based on Geo-parcels[J]. Journal of Geo-information Science, 2020, 22(1): 57 Copy Citation Text show less
    References

    [1] 宫鹏. 对遥感科学应用的一点看法[J]. 遥感学报, 2019,23(4):567-569. [ GongP. Towards more extensive and deeper application of remote sensing[J]. Journal of Remote Sensing, 2019,23(4):567-569. ] [ Gong P. Towards more extensive and deeper application of remote sensing[J]. Journal of Remote Sensing, 2019,23(4):567-569. ]

    [2] 李德仁, 张良培, 夏桂松. 遥感大数据自动分析与数据挖掘[J]. 测绘学报, 2014,43(12):1211-1216. [ Li DR, Zhang LP, Xia GS. Automatic analysis and mining of remote sensing big data[J]. Acta Geodaetica Et Cartographica Sinica, 2014,43(12):1211-1216. ] [ Li D R, Zhang L P, Xia G S. Automatic analysis and mining of remote sensing big data[J]. Acta Geodaetica Et Cartographica Sinica, 2014,43(12):1211-1216. ]

    [3] 骆剑承, 吴田军, 李均力, 等. 遥感图谱认知[M]. 北京: 科学出版社, 2017. [ Luo JC, Wu TJ, Li JL, et al.Spatial-spectral cognition of remote sensing[M]. Beijing: Science Press, 2017. ] [ Luo J C, Wu T J, Li J L, et al. Spatial-spectral cognition of remote sensing[M]. Beijing: Science Press, 2017. ]

    [4] Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2-16(2010).

    [5] 骆剑承, 吴田军, 夏列钢. 遥感图谱认知理论与计算[J]. 地球信息科学学报, 2016,18(5):578-589. [ Luo JC, Wu TJ, Xia LG. The theory and calculation of spatial-spectral cognition of remote Sensing[J]. Journal of Geo-information Science, 2016,18(5):578-589. ] [ Luo J C, Wu T J, Xia L G. The theory and calculation of spatial-spectral cognition of remote Sensing[J]. Journal of Geo-information Science, 2016,18(5):578-589. ]

    [6] 李秦, 高锡章, 张涛, 等. 最优分割尺度下的多层次遥感地物分类实验分析[J]. 地球信息科学学报, 2011,13(3):409-417. [ LiQ, Gao XZ, ZhangT, et al. Optimal segmentation scale selection and evaluation for multi-layer image recognition and classification[J]. Journal of Geo-information Science, 2011,13(3):409-417. ] [ Li Q, Gao X Z, Zhang T, et al. Optimal segmentation scale selection and evaluation for multi-layer image recognition and classification[J]. Journal of Geo-information Science, 2011,13(3):409-417. ]

    [7] 陶超, 谭毅华, 蔡华杰, 等. 面向对象的高分辨率遥感影像城区建筑物分级提取方法[J]. 测绘学报, 2010,39(1):39-45. [ TaoC, Tan YH, Cai HJ, et al. Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery[J]. Acta Geodaetica Et Cartographica Sinica, 2010,39(1):39-45. ] [ Tao C, Tan Y H, Cai H J, et al. Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery[J]. Acta Geodaetica Et Cartographica Sinica, 2010,39(1):39-45. ]

    [8] et alGeographic object-based image analysis-towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191(2014).

    [9] 自然资源部. 第三次全国国土调查实施方案[R]. 2018年18号文件, 2018. [ Ministry of Natural Resources. Third national land survey implementation plan[R]. Document No.18 in 2018, 2018. ] [ Ministry of Natural Resources. Third national land survey implementation plan[R]. Document No.18 in 2018, 2018. ]

    [10] Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440(2015).

    [11] Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 4, 22-40(2016).

    [12] Generalized boundaries from multiple image interpretations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1312-1324(2014).

    [13] Pixel-wise deep learning for contour detection[C]. Proceedings of the International Conference on Learning Representation (ICLR)(2015).

    [14] et alRicher convolutional features for edge detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3000-3009(2017).

    [15] Unsupervised segmentation of color-texture regions in images and video[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 800-810(2001).

    [16] et alDeep filter banks for texture recognition, description, and segmentation[J]. International Journal of Computer Vision, 118, 65-94(2016).

    [17] 李志刚, 张小勇, 艾廷华. 土地利用图斑综合研究[J]. 地理空间信息, 2004,2(3):13-18. [ Li ZG, Zhang XY, Ai TH. Generalization research of land use patch[J]. Geospatial Information, 2004,2(3):13-18. ] [ Li Z G, Zhang X Y, Ai T H. Generalization research of land use patch[J]. Geospatial Information, 2004,2(3):13-18. ]

    [18] Learning building extraction in aerial scenes with convolutional networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 40, 2793-2798(2017).

    [19] Deep learning approach for building detection in satellite multispectral imagery[C]. Proceedings of 9 th IEEE International Conference on Intelligent Systems, 1-5(2018).

    [20] Quantitative remote sensing of land surfaces[M]. Hoboken: John Wiley & Sons Inc.(2005).

    [21] et alMeasuring phenological variability from satellite imagery[J]. Journal of vegetation science, 5, 703-714(1994).

    [22] et alMulti-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 36, 1072-1087(1998).

    [23] 童庆禧, 张兵, 郑兰芬. 高光谱遥感:原理,技术与应用[M]. 北京: 高等教育出版社, 2006. [ Tong QX, ZhangB, Zhen LF. Hyperspectral remote sensing: Principle, technology and application[M]. Beijing: Higher Education Press, 2006. ] [ Tong Q X, Zhang B, Zhen L F. Hyperspectral remote sensing: Principle, technology and application[M]. Beijing: Higher Education Press, 2006. ]

    [24] Scale issues in remote sensing: A review on analysis, processing and modeling[J]. Sensors, 9, 1768-1793(2009).

    [25] et alUsing spatial interpolation to construct a comprehensive archive of Australian climate data[J]. Environmental Modelling & Software, 16, 309-330(2001).

    [26] et alUncertainty quantification of interpolated maps derived from observations with different accuracy levels[C]. Proceedings of Spatial Accuracy 2016 (12 th Int. Symp. Spatial Accuracy Assessment Natural Resources Environmental Sciences), Montpellier, 49-51(2016).

    [27] et alMapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions[J]. PLoS One, 10, e0125814(2015).

    [28] et alSpatially disaggregated population estimates in the absence of national population and housing census data[J]. Proceedings of the National Academy of Sciences, 115, 3529-3537(2018).

    [29] 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报, 2019,74(3):586-598. [ PeiT, Liu YX, Guo SH, et al. Principle of big geodata mining[J]. Acta Geographica Sinica, 2019,74(3):586-598. ] [ Pei T, Liu Y X, Guo S H, et al. Principle of big geodata mining[J]. Acta Geographica Sinica, 2019,74(3):586-598. ]

    [30] 李德仁, 王树良, 史文中, 等. 论空间数据挖掘和知识发现[J]. 武汉大学学报·信息科学版, 2001,26(6):491-499. [ Li DR, Wang SL, Shi WZ, et al. On spatial data mining and knowledge discovery[J]. Geomatics and Information Science of Wuhan University, 2001,26(6):491-499. ] [ Li D R, Wang S L, Shi W Z, et al. On spatial data mining and knowledge discovery[J]. Geomatics and Information Science of Wuhan University, 2001,26(6):491-499. ]

    [31] 李德仁, 王树良, 李德毅, 等. 论空间数据挖掘和知识发现的理论与方法[J]. 武汉大学学报·信息科学版, 2002,27(3):221-233. [ Li DR, Wang SL, Li DY, et al. Theories and technologies of spatial data mining and knowledge discovery[J]. Geomatics and Information Science of Wuhan University, 2002,27(3):221-233. ] [ Li D R, Wang S L, Li D Y, et al. Theories and technologies of spatial data mining and knowledge discovery[J]. Geomatics and Information Science of Wuhan University, 2002,27(3):221-233. ]

    [32] An assessment of support vector machines for land cover classification[J]. International Journal of Remote Sensing, 23, 725-749(2002).

    [33] Time series data mining: identifying temporal patterns for characterization and prediction of time series events[M]. Milwaukee: Marquette University(1999).

    [34] 陈劲松, 黄健熙, 林珲, 等. 基于遥感信息和作物生长模型同化的水稻估产方法研究[J]. 中国科学:信息科学, 2010,S1(40):173-183. [ Chen JS, Huan JX, LinH, et al. Rice yield estimation by assimilation remote sensing into crop growth model[J]. Science China: Information Sciences, 2010,S1(40):173-183. ] [ Chen J S, Huan J X, Lin H, et al. Rice yield estimation by assimilation remote sensing into crop growth model[J]. Science China: Information Sciences, 2010,S1(40):173-183. ]

    [35] et alTopographic constrained land cover classification in mountain areas using fully convolutional network[J]. International Journal of Remote Sensing, 18, 7127-7152(2019).

    [36] et alA WTLS-based method for remote sensing imagery registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 102-116(2015).

    [37] et alA long time series radiometric normalization method for Landsat images[J]. Sensors, 18, 4504(2018).

    [38] et alA thin-cloud mask method for remote sensing images based on sparse dark pixel region detection[J]. Remote Sensing, 10, 617(2018).

    [39] et alMonitoring land-cover changes: A machine-learning perspective[J]. IEEE Geoscience and Remote Sensing Magazine, 4, 8-21(2016).

    [40] et alGeo-parcel based crop identification by integrating high spatial-temporal resolution imagery from multi-source satellite data[J]. Remote Sensing, 9, 1298(2017).

    [41] et alGeo-parcel-based geographical thematic mapping using C5.0 decision tree: A case study of evaluating sugarcane planting suitability[J]. Earth Science Informatics, 12, 57-70(2019).

    [42] Holistically-nested edge detection[C]. Proceedings of the IEEE International Conference on Computer Vision, 1395-1403(2015).

    [43] D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 182-186(2018).

    [44] U-net: Convolutional networks for biomedical image segmentation[C]. Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 234-241(2015).

    [45] et alGoing deeper with convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9(2015).

    [46] Optimal decisions from probabilistic models: the intersection-over-union case[C]. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 548-555(2014).

    [47] et alLong-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data[J]. GIScience & Remote Sensing, 8, 1170-1191(2019).

    [48] et alTarget classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1-12(2016).

    [49] et alDeep recurrent neural network for agricultural classification using multitemporal SAR sentinel-1 for Camargue, France[J]. Remote Sensing, 10, 1217(2018).

    [50] et alDCN-based spatial features for improving parcel-based crop classification using high-resolution optical images and multi-temporal SAR data[J]. Remote Sensing, 11, 1619(2019).

    [51] Random forests[J]. Machine Learning, 45, 5-32(2001).

    [52] 傅伯杰, 刘焱序. 系统认知土地资源的理论与方法[J]. 科学通报, 2019,64(21):2172-2179. [ Fu BJ, Liu YX. The theories and methods for systematically understanding land resource[J]. Chinese Science Bulletin, 2019,64(21):2172-2179. ] [ Fu B J, Liu Y X. The theories and methods for systematically understanding land resource[J]. Chinese Science Bulletin, 2019,64(21):2172-2179. ]

    Jiancheng LUO, Tianjun WU, Zhifeng WU, Ya'nan ZHOU, Lijing GAO, Yingwei SUN, Wei WU, Yingpin YANG, Xiaodong HU, Xin ZHANG, Zhanfeng SHEN. Methods of Intelligent Computation and Pattern Mining based on Geo-parcels[J]. Journal of Geo-information Science, 2020, 22(1): 57
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