[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. ]