[1]WorldView-3 Satellite Sensor(0.31m)[EB/OL]. https://www. satimagingcorp. com/satellite sensors/worldview-3/.

[2]CHENG Y, JIN S, WANG M, et al. Image Mosaicking Approach for a Double-Camera System in the GaoFen2 Optical Remote Sensing Satellite Based on the Big Virtual Camera[J]. Sensors, 2017, 17(6):1441.

[3]JI L, WANG J, GENG X, et al. Probabilistic Graphical Model Based Approach for Water Mapping Using GaoFen-2(GF-2)High Resolution Imagery and Landsat 8 Time Series[EB/OL]. http://arxiv. org/abs/1612.07801.

[4]飞鸟的博客．中国发布“高分二号”卫星亚米级高分辨率卫星图[EB/OL]．http://blog．sina．com.cn/s/blog_57/64/eeo/02v2hj．html.

[5]中国仪表网．倾斜数字航摄仪三峡库区首飞成功助力地质调查[EB/OL]．http://www．ybzhan．cn/news/detail/66802．html.

[6]AS vision Limited. True Orthophoto[EB/OL]. http://www. as-vision. net/trueorthophoto/.

[7]MACQUEEN J. Some Methods for Classification and Analysis of MultiVariate Observations[C]. Proc of Berkeley Symposium on Mathematical Statistics&Probability, 1965.

[8]赵越，周萍．改进的K-means算法在遥感图像分类中的应用[J]．国土资源遥感，2011，02:87-90.

[9]WONG J A, HARTIGANM A. Algorithm AS 136:A K-Means Clustering Algorithm[J]. Journalof the Royal Statistical Society. Series C（Applied Statistics）, 1979, 28（1）:100-108.

[10]WAGSTAFF K, CARDIE C. Constrained K-means Clusteringwith Background Knowledge[C]. Proceedings of the ICML, 2001:577-584.

[11]PFNA J M, LOZANO J A, LARRANA P. An Empirical Comparison of Four Initialization Methods for the K-means Algorithm[J]. Pattern Recognition Letters, 1999, 20(10):1027-1040.

[12]赵红丹，田喜平．基于K-means算法分割遥感图像的阈值确定方法研究[J]．科学技术与工程，2017，09:250-254.

[13]纵清华，王志宇，过仲阳，等．基于小波变换和K-means算法的遥感影像分类[J]．杭州师范大学学报（自然科学版），2016，15（2）:203-207.

[14]LEICHTLE T, GEISS C, LAKEST, et al. Class Imbalancein Unsupervised Change Detection—A Diagnostic Analysis from Urban Remote Sensing[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 60:83-98.

[15]ABDULLAHI S, SCHARDT M, PRETZSCH H. An Unsupervised Two-stage Clustering Approach for Forest Structure Classification Based on X-band InSAR Data—A Case Study in Complex Temperate Forest Stands[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 57:36-48.

[16]YANG W, HOU K, LIU B, et al. Two-Stage Clustering Technique Based on the Neighboring Union Histogram for Hyperspectral Remote Sensing Images[J]. IEEE Access, 2017, 5:5640-5647.

[17]VINCENT L, SOILLE P. Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13(6):583-598.

[18]GRAU V, MEWES A U J, ALCANIZ M, et al. Improved Watershed Transform for Medical Image Segmentation Using Prior Information[J]. IEEE Transactions on Med Imaging, 23(4):447-458.

[19]张建廷，张立民．结合光谱和纹理的高分辨率遥感图像分水岭分割[J]．武汉大学学报（信息科学版），2017，04:449-455.

[20]赵宗泽，张永军．基于植被指数限制分水岭算法的机载激光点云建筑物提取[J]．光学学报，2016，10:503-511.

[21]赵栋．改进的分水岭变换高分辨率遥感影像分割方法研究[D]．北京：中国地质大学，2016.

[22]LI D R, ZHANG G F, WU Z C, et al. An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation[J]. IEEE Transactions on Image Processing, 2010, 19（10）:2781-2787.

[23]WANG G, MENG Y, SAHLI H, et al. Vehicles Detection UsingGF-2 Imagery Based on Watershed Image Segmentation[C]. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium（IGARSS）, 2016.

[24]KAVZOGLU T, TONBUL H. A Comparative Study of Segmentation Quality for Mult-i resolution Segmentation and Watershed Transform [C]. Proceedings of the 8th International Conference on Recent Advances in Space Technologies （RAST）, 2017.

[25]ZHANG T, LEI B, GAN Y, et al. Evaluation of Small Watershed Management Efforts Using ZY-3 Satellite Images—A Case Stduy in the Water Source Area of Middle Route of South to North Water Diversion project [C ]. IEEE International Geoscience and Remote Sensing Symposium （IGARSS）, 2017.

[26]CHAN T, VESE L. An Active Contour Model Without Edges [J]. Scale-Space Theories in Computer Vision, 1999, 16(82):141-151.

[27]KUMAR R. Snakes: Active Contour Models [J]. International Journal of Computer Vision, 1988, 1（4）: 321-331.

[28]CASELLES V, KIMMEL R, SAPIRO G. Geodesic Active Contour[J]. International Journal of Computer Vision, 1997, 22（1）: 61-79.

[29]NIU S J, CHEN Q, DE SISTERNES L, et al. Robust Noise Region-based Active Contour Model via Local Similarity Factor for Image Segmentation [J]. Pattern Recognition, 2017, 61:104-119.

[30]LI C M, GORE J C, DAVATZIKOS C. Multiplicative Intrinsic Component Optimization （MICO） for MRI Bias Field Estimation and Tissue Segmentation [J]. Magnetic Resonance Imaging, 2014, 32（7）: 913-923.

[31]刘建磊，隋青美，朱文兴．结合概率密度函数和主动轮廓模型的磁共振图像分割[J]．光学精密工程，2014，12:3435-3443.

[32]ZHANG K H, ZHANG L, SONG H H, et al. Active Contours with Selective Local or Global Segmentation: A New Formulation and Level Set Method [J]. Image and Vision Computing, 2010, 28（4）: 668-676.

[33]HOOGI A, SUBRAMANIAM A, VEERAPANENI R, et al. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis [J ]. IEEE Transactions on Medical Imaging, 2017, 36（3）: 781-791.

[34]苏丽，吴俊杰，庞迪．基于改进主动轮廓模型的全景海天线检测[J]．光学学报，2016，11:189-196.

[35]姜大伟，范剑超，黄凤荣．SAR图像海岸线检测的区域距离正则化几何主动轮廓模型[J]．测绘学报，2016，09:1096-1103.

[36]HAN B, WU Y. A Novel Active Contour Model Based on Modified Symmetric Cross Entropy for Remote Sensing River Image Segmentation [J]. Pattern Recognition, 2017, 67:396-409.

[37]TIAN B S, LI Z, ZHANG M M, et al. Mapping Thermokarst Lakes on the Qingha-i Tibet Plateau Using Nonlocal Active Contours in Chinese GaoFen-2 Multispectral Imagery [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10（5）: 1687-1700.

[38]SHEN J. Connectionism in AI and Grammatical Theories [J]. Journal of Foreign Languages, 2004,3: 2-10.

[39]冯天瑾．神经网络技术[M]．青岛：中国海洋大学出版社，1994.

[40]王学，刘全明，马腾．西北寒旱灌区裸露地表粗糙度SAR反演建模方法研究[J]．灌溉排水学报，2017，06:74-80.

[41]冼翠玲，张艳军，张明琴，等．基于高分辨率多光谱影像的温瑞塘河水质反演模型研究[J]．中国农村水利水电，2017，03:90-95.

[42]吐热尼古丽，张晓帆．基于人工神经网络的遥感图像分类研究[J]．长春师范大学学报，2006，02:81-84.

[43]喻俊，李晓敏，张权，等．基于高光谱遥感的植被冠层氮素反演方法研究进展[J]．陕西林业科技，2016，06:93-97.

[44]YUAN H H, YANG G J, LI C C, et al. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models [J]. Remote Sensing, 2017, 9（4）: 309-323.

[45]ZHENG X Q, SHEN F J, ZHENG S N, et al. Estimation of Regional Evapotranspiration and Biomass Production from Remote Sensing Data by Artificial Neural Network （ANN） method [J]. Journal of Food Agriculture & Environment, 2012, 10（3-4）: 1558-1561.

[46]PRABU S, RAMAKRISHNAN S S. Combined Use of Socio Economic Analysis, Remote Sensing and GIS Data for Landslide Hazard Mapping Using ANN [J]. Journal of the Indian Society of Remote Sensing, 2009, 37（3）: 409-421.

[47]VAPNIK V N．统计学习理论的本质[M]．张学工，译．北京：清华大学出版社，2000.

[48]VAPNIK V N. An Overview of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5):988-999.

[49]MÜLLER K R, MIKA S, et al. An Introduction to Kernel-based Learning Algorithms[J]. IEEE Transactions on Neural Networks, 2001, 12(2):181.

[50]胡根生，吴问天，罗菊花，等．结合HJ卫星影像和最小二乘孪生支持向量机的小麦蚜虫遥感监测[J]．浙江大学学报（农业与生命科学版），2017，02:211-219.

[51]刘伟，赵庆展，汪传建，等．基于最小二乘支持向量机的无人机遥感影像分类[J]．江苏农业科学，2017，09:195-199.

[52]张静．西北旱区遥感影像分类方法研究[D]。咸阳：西北农林科技大学，2016.

[53]MACK B, WASKE B. In-depth Comparisons of MaxEnt, Biased SVM and One-class SVM for One-class Classification of Remote Sensing Data [J]. Remote Sensing Letters, 2017, 8 （3 ）: 290-299.

[54]SUKAWATTANAVIJIT C, CHEN J, ZHANG H S. GA-SVM Algorithm for Improving Land-Cover Classification Using SAR and Optical Remote Sensing Data [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14（3）: 284-288.

[55]LI D W, YANG F B, WANG X X. Study on Ensemble Crop Information Extraction of Remote Sensing Images Based on SVM and BPNN [J]. Journal of the Indian Society of Remote Sensing, 2017, 45（2）: 229-237.

[56]GASMI A, GOMEZ C, ZOUARI H, et al. PCA and SVM as Geo-computational Methods for Geological Mapping in the Southern of Tunisia, Using ASTER Remote Sensing Data Set [J]. Arabian Journal of Geosciences, 2016, 9(20):753.

[57]BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32.

[58]沈润平，郭佳，张婧娴，等．基于随机森林的遥感干旱监测模型的构建[J]．地球信息科学学报，2017，01:125-133.

[59]齐雁冰，王茵茵，陈洋，等．基于遥感与随机森林算法的陕西省土壤有机质空间预测[J]．自然资源学报，2017，06:1074-1086.

[60]姚明煌，骆炎民．改进的随机森林及其在遥感图像中的应用[J]．计算机工程与应用，2016，04:168-173.

[61]CANOVAS-GARCIA F, ALONSO-SARRIA F, GOMARIZ-CASTILLO F, et al. Modification of the Random Forest Algorithm to Avoid Statistical Dependence Problems when Classifying Remote Sensing Imagery [J]. Computers & Geosciences, 2017, 103:1-11.

[62]KIM J, GRUNWALD S. Assessment of Carbon Stocks in the Topsoil Using Random Forest and Remote Sensing Images [J]. Journal of Environmental Quality, 2016, 45（6）: 1910-1918.

[63]LUO Y M, HUANG D T, LIU P Z, et al. An Novel Random Forests and Its Application to the Classification of Mangroves Remote Sensing Image [J]. Multimedia Tools and Applications, 2016, 75（16）: 9707-9722.

[64]TRAMONTANA G, ICHII K, CAMPS-VALLS G, et al. Uncertainty Analysis of Gross Primary Production Upscaling Using Random Forests, Remote Sensing and Eddy Covariance Data [J]. Remote Sensing of Environment, 2015, 168:360-373.

[65]FENG Q L, LIU J T, GONG J H. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis [J]. Remote Sensing, 2015, 7（1）: 1074-1094.

[66]HINTON G E, SALAKHUTDINOV R R. Reducing the Dimensionality of Data with Neural Networks [J]. Science, 2006, 313（5786）: 504-507.

[67]LECUN Y, BENGIO Y, HINTON G. Deep Learning [J]. Nature, 2015, 521（7553）: 436-544.

[68]YE J, NI J Q, YI Y. Deep Learning Hierarchical Representations for Image Steganalysis [J]. IEEE Transactions on Information Forensics and Security, 2017, 12（11）: 2545-2557.

[69]LI R J, ZENG T, PENG H C, et al. Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction [J]. IEEE Transactions on Medical Imaging, 2017, 36（7）: 1533-1541.

[70]ZHANG K, ZUO W M, CHEN Y J, et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [J]. IEEE Transactions on Image Processing, 2017, 26（7）: 3142-3155.

[71]LU N, LI T F, REN X D, et al. A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25（6）: 566-576.

[72]SHAO L, CAI Z Y, LIU L, et al. Performance Evaluation of Deep Feature Learning for RGB-D image/video Classification [J]. Information Sciences, 2017, 385:266-283.

[73]PEREZ M, AVILA S, MOREIRA D, et al. Video Pornography Detection Through Deep Learning Techniques and Motion Information [J]. Neurocomputing, 2017, 230:279-293.

[74]XUE H Y, LIU Y, CAI D, et al. Tracking People in RGBD Videos Using Deep Learning and Motion Clues [J]. Neurocomputing, 2016, 204:70-76.

[75]KAHOU S E, BOUTHILLIER X, LAMBLIN P, et al. EmoNets: Multimodal Deep Learning Approaches for Emotion Recognition in Video [J]. Journal on Multimodal User Interfaces, 2016, 10 （2）: 99-111.

[76]GUO H Y, WANG J Q, LU H Q. Multiple Deep Features Learning for Object Retrieval in Surveillance Videos [J]. IET Computer Vision, 2016, 10（4）: 268-272.

[77]YAN W W, TANG D, LIN Y J. A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and Its Application [J]. IEEE Transactions on Industrial Electronics, 2017, 64（5）: 4237- 4245.

[78]KUSSUL N, LAVRENIUK M, SKAKUN S, et al. Deep Learning Classification of Land Cover andCrop Types Using Remote Sensing Data [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14（5）: 778-782.

[79]BAUMANN K, SCHNEIDER G. Big Data and Deep Learning: A New Age of Molecular Informatics? [J]. Molecular Informatics, 2017, 36(1-2):178.

[80]ZHANG Q C, YANG L T, CHEN Z K. Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning [J]. IEEE Transactions on Computers, 2016, 65（5）: 1351-1362.

[81]WANG L, OUYANG W, WANG X, et al. Visual Tracking with Fully Convolutional Networks [C]. Proceedings of the 2015 IEEE International Conference on Computer Vision （ICCV）, 2016.

[82]LONG J, SHELHAMER E, DARRELL T. Fully Convolutional Networks for Semantic Segmentation[C]. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, 2015.

[83]SHELHAMER E, LONG J, DARRELL T. Fully Convolutional Networks for Semantic Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39（4）: 640-651.

[84]HUANG W Q, GONG X J, YANG M Y. Joint Object Segmentation and Depth Upsampling [J]. IEEE Signal Processing Letters, 2015, 22（2）: 192-196.

[85]DUMOULIN V, VISIN F. A Guide to Convolution Arithmetic for Deep Learning [EB/OL]. https://arxiv.org/abs/1603.07285v1.2016.

[86]AHMAD T, CAMPR P, C ˇADIK M, et al. Comparison of Semantic Segmentation Approaches for Horizon/sky Line Detection[C]. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN),2017.

[87]BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39（12）: 2481-2495.

[88]SHEN F, GAN R. Joint Segmentation and Classification with Fully Convolutional Networks[C]. Proceedings of the 3rd International Conference on Systems and Informatics (ICSAI),2016.

[89]SHUAI B, ZUO Z, WANG B, et al. Scene Segmentation with DAG-Recurrent Neural Networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40: 1480-1493.

[90]WANG Y, WANG C, ZHANG H. IntegratingH-A-α with fully convolutional networks for fully PolSAR classification[C]. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing （RSIP）, 2017.

[91]ALSHEHHI R, MARPU P R, WOON W L, et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks [J ]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:139-149.

[92]LIN H, SHI Z, ZOU Z. Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 10: 1-5.

[93]JIAO L, LIANG M, CHEN H, et al. Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55（10）: 5585-5599. 3mPW8Tk8RlVjVIAS5d7KQIuunB5s47xZYlOTomnEj8FVX+EGIGtAZikY6vECx+qE