|
|
- 2018
遥感大数据时代与智能信息提取
|
Abstract:
近年来，天地一体化对地观测系统与智能计算技术的快速发展为遥感科技进步甚至变革提供了难得的机遇。遥感信息技术在历经20世纪60~80年代以统计数学模型为核心的数字信号处理时代、从90年代至今以遥感信息物理量化为标志的定量遥感时代之后，现在正逐渐进入一个以数据模型驱动、大数据智能分析为特征的遥感大数据时代。在总结遥感信息技术历史发展脉络的基础上，阐述了遥感大数据的内涵和智能信息提取的时代特点，并从遥感大数据的理念出发，论述了面向对象的遥感知识库构建，分析了融合遥感知识和深度学习算法的大数据智能信息提取策略。通过典型实例，介绍了以深度学习为代表的智能算法在遥感大数据目标检测、精细分类、参数反演等方面的发展现状与趋势，并讨论了深度学习在遥感大数据时代的智能信息提取方面的应用潜力
| [1] | Goward S N, Huemmrich K F. Vegetation Canopy PAR Absorptance and the Normalized Difference Vegetation Index:An Assessment Using the SAIL Model[J]. Remote Sensing of Environment, 1992, 39(2):119-140 |
| [2] | Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems, 2012, 25(2):1097-1105 |
| [3] | Wang S, Quan D, Liang X, et al. A Deep Learning Framework for Remote Sensing Image Registration[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2018, https://doi.org/10.1016/j.isprsjprs.2017.12.012 |
| [4] | Guidici D, Clark M L. One-Dimensional Convolutional Neural Network Land-cover Classification of Multi-seasonal Hyperspectral Imagery in the San Francisco Bay Area, California[J].Remote Sensing, 2017, 9(6):629 |
| [5] | Girshick R. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA,2014 |
| [6] | Ren S, He K, Girshick R. Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149 |
| [7] | Tang J, Deng C, Huang G, et al. Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1174-1185 |
| [8] | Cheng G, Zhou P, Han J. Learning Rotation-Inva-riant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images[J].IEEE Transactions on Geoscience and Remote Sensing, 2016,54(12):7405-7415 |
| [9] | Chen L, Papandreou G, Kokkinos I, et al. DeepLab:Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848 |
| [10] | Lyzenga D R. Passive Remote Sensing Techniques for Mapping Water Depth and Bottom Features[J].Applied Optics, 1978, 17(3):379-383 |
| [11] | Strahler A H. The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data[J].Remote Sensing of Environment, 1980, 10(2):135-163 |
| [12] | Miller J, Hare E W, Wu J. Quantitative Characte-rization of the Vegetation Red Edge Reflectance 1. An Inverted-Gaussian Reflectance Model[J].International Journal of Remote Sensing, 1990, 11(10):1755-1773 |
| [13] | Zhang Bing. Intelligent Remote Sensing Satellite System[J].Journal of Remote Sensing, 2011, 15(3):415-431(张兵. 智能遥感卫星系统[J]. 遥感学报, 2011, 15(3):415-431) |
| [14] | Ma Y, Wu H, Wang L, et al. Remote Sensing Big Data Computing:Challenges and Opportunities[J].Future Generation Computer Systems, 2015, 51:47-60 |
| [15] | Wang H, Wang Y, Zhang Q, et al. Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images[J]. Remote Sensing, 2017, 9(5):446 |
| [16] | Xu Z, Xu X, Wang L, et al. Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery[J].Remote Sen-sing, 2017, 9(12):1312 |
| [17] | Zou Z, Shi Z. Random Access Memories:A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images[J].IEEE Transactions on Image Processing, 2018, 27(3):1100-1111 |
| [18] | Deng J, Dong W, Socher R, et al. ImageNet:A Large-Scale Hierarchical Image Database[C].IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA,2009 |
| [19] | Karalas K, Tsagkatakis G, Zervakis M, et al. Deep Learning for Multi-label Land Cover Classification[C]. SPIE Remote Sensing, Toulouse, France,2015 |
| [20] | Xia G S, Yang W, Delon J, et al. Structural High-Resolution Satellite Image Indexing[C]. ISPRS TC Ⅶ Symposium-100 Years ISPRS,Vienna, Austria,2010 |
| [21] | Campos-Taberner M, Romero-Soriano A, Gatta C, et al. Processing of Extremely High-Resolution LiDAR and RGB Data:Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A:2-D Contest[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(12):5547-5559 |
| [22] | Liu W, Anguelov D, Erhan D, et al. SSD:Single Shot MultiBox Detector[C].European Conference on Computer Vision, Springer, Cham, 2016 |
| [23] | He K, Zhang X, Ren S, et al. Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916 |
| [24] | Jiang Q, Cao L, Cheng M, et al. Deep Neural Networks-based Vehicle Detection in Satellite Images[C].International Symposium on Bioelectronics and Bioinformatics, Beijing, China, 2015 |
| [25] | Chen Z, Zhang T, Ouyang C. End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images[J].Remote Sensing, 2018, 10(1):139 |
| [26] | Makantasis K, Karantzalos K, Doulamis A, et al. Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks[C].IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015 |
| [27] | Zhao W, Du S. Spectral-Spatial Feature Extraction for Hyperspectral Image Classification:A Dimension Reduction and Deep Learning Approach[J].IEEE Transactions on Geoscience and Remote Sen-sing, 2016, 54(8):4544-4554 |
| [28] | Shen Qian, Zhu Li, Cao Hongye. Remote Sensing Monitoring and Screening for Urban Black and Odorous Water Boy:A Review[J].Chinese Journal of Applied Ecology, 2017, 28(10):3433-3439(申茜, 朱利, 曹红业. 城市黑臭水体遥感监测与筛查研究进展[J].应用生态学报, 2017, 28(10):3433-3439) |
| [29] | Lecun Y, Bottou L, Bengio Y, et al. Gradient-Based Learning Applied to Document Recognition[J].Proceedings of the IEEE, 1998, 86(11):2278-2324 |
| [30] | Santara A, Mani K, Hatwar P, et al. Bass Net:Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9):5293-5301 |
| [31] | Marcos D,Hamid R,Tuia D. Geospatial Correspondence for Multimodal Registration[C]. IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, USA,2016 |
| [32] | Zhang Bing. Current Status and Future Prospects of Remote Sensing[J].Bulletin of Chinese Academy of Sciences, 2017, 32(7):774-784(张兵. 当代遥感科技发展的现状与未来展望[J].中国科学院院刊, 2017, 32(7):774-784) |
| [33] | Li Deren, Shen Xin, Ma Hongchao, et al. Commercial Operation of China's High-Resolution Earth Observation System is Imperative[J].Geomatics and Information Science of Wuhan University, 2014, 39(4):386-390(李德仁, 沈欣, 马洪超, 等. 我国高分辨率对地观测系统的商业化运营势在必行[J].武汉大学学报·信息科学版,2014, 39(4):386-390) |
| [34] | Zou Q, Ni L, Zhang T, et al. Deep Learning Based Feature Selection for Remote Sensing Scene Classification[J].IEEE Geoscience and Remote Sensing Letters, 2015, 12(11):2321-2325 |
| [35] | He K, Zhang X, Ren S, et al. Deep Residual Lear-ning for Image Recognition[C].IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA,2016 |
| [36] | Verrelst J, Caicedo J P R, Mu？oz-Marí J, et al. SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluo-rescence Spectra[J]. Remote Sensing, 2017, 9(9):927 |
| [37] | Li Xiaowen. Retrospect, Prospect and Innovation in Quantitative Remote Sensing[J].Journal of Henan University (Natural Sciences), 2005, 35(4):49-58(李小文. 定量遥感的发展与创新[J].河南大学学报(自然科学版), 2005, 35(4):49-58) |
| [38] | Li Fenling, Chang Qingrui, Liu Jiaqi, et al. SVM Classification with Multi-texture Data of ZY-102C HR Image[J].Geomatics and Information Science of Wuhan University, 2016, 41(4):455-462(李粉玲, 常庆瑞, 刘佳岐, 等. 基于多纹理和支持向量机的ZY-102C星HR数据分类[J].武汉大学学报·信息科学版,2016, 41(4):455-462) |
| [39] | Freund Y, Schapire R E. A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting[J]. Journal of Computer and System Sciences, 1997, 55(1):119-139 |
| [40] | Hinton G E, Osindero S, Teh Y W. A Fast Lear-ning Algorithm for Deep Belief Nets[J].Neural Computation, 2006, 18(7):1527-1554 |
| [41] | You Q Z, Luo J B, Jin H L, et al. Building a Large Scale Dataset for Image Emotion Recognition:The Fine Print and the Benchmark[C]. Thirtieth AAAI Conference on Artificial Intelligence,Phoenix, USA,2016 |
| [42] | John E B, Derek T A, Chee S C. A Comprehensive Survey of Deep Learning in Remote Sensing:Theories, Tools and Challenges for the Community[J].Journal of Applied Remote Sensing, 2017, 11(4):042609 |
| [43] | Cramer M. The DGPF-Test on Digital Airborne Camera Evaluation-Overview and Test Design[J]. Photogrammetry-Foregrounding-Geoinformation, 2010, 2010(2):73-82 |
| [44] | Girshick R. Fast R-CNN[C].IEEE International Conference on Computer Vision, Santigago, Chile, 2015 |
| [45] | Zhao H, Shi J, Qi X, et al. Pyramid Scene Parsing Network[C].IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA,2017 |
| [46] | Hu W, Huang Y, Wei L, et al. Deep Convolutional Neural Networks for Hyperspectral Image Classification[J]. Journal of Sensors, 2015,2015(2):1-12 |
| [47] | Li Y, Zhang H, Shen Q. Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network[J].Remote Sensing, 2016, 18(7):1527-1554 |
| [48] | Li Xiaowen,Wang Jindi, Hu Baoxin, et al. Role of Prior Knowledge in Remote Sensing Inversion[J]. Science in China(Series D), 1998, 28(1):67-73(李小文, 王锦地, 胡宝新,等. 先验知识在遥感反演中的作用[J].中国科学(D辑),1998, 28(1):67-73) |
| [49] | Liu L, Tang H, Caccetta P, et al. Mapping Affo-restation and Deforestation from 1974 to 2012 Using Landsat Time-Series Stacks in Yulin District, a Key Region of the Three-North Shelter Region, China[J].Environmental Monitoring and Assessment, 2013, 185(12):9949-9965 |
| [50] | Yu X, Lu H, Liu Q. Role of Prior Knowledge in Remote Sensing Inversion[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 172:188-193 |
| [51] | Xia G S, Bai X, Ding J, et al. DOTA:A Large-Scale Dataset for Object Detection in Aerial Images[OL]. https://arxiv.org/abs/1711.10398v2,2017 |
| [52] | Chen X, Xiang S, Liu C, et al. Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks[C].Asian Conference on Pattern Recognition, Naha, Japan, 2013 |
| [53] | Chen X, Xiang S, Liu C. Vehicle Detection in Satel-lite Images by Hybrid Deep Convolutional Neural Networks[J].IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1797-1801 |
| [54] | Zhang F, Du B, Zhang L, et al. Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9):5553-5563 |
| [55] | Radovic M, Adarkwa O, Wang Q. Object Recognition in Aerial Images Using Convolutional Neural Networks[J].Journal of Imaging, 2017, 3(2):21 |
| [56] | Long J, Evan S, Trevor D. Fully Convolutional Networks for Semantic Segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, United States, 2015 |
| [57] | Gómez-Dans J L, Lewis P E, Disney M. Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences[J].Remote Sensing, 2016, 8(2):119 |
| [58] | Liu L, Song B, Zhang S, et al. A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents[J]. Remote Sensing, 2017, 9(11):1113 |
| [59] | Liu Z, Wang H, Weng L, et al. Ship Rotated Bounding Box Space for Ship Extraction from High-Resolution Optical Satellite Images with Complex Backgrounds[J].IEEE Geoscience and Remote Sensing Letters, 2017, 13(8):1074-1078 |
| [60] | Razakarivony S, Jurie F. Vehicle Detection in Aerial Imagery:A Small Target Detection Benchmark[J]. Journal of Visual Communication and Image Representation, 2015, 34:187-203 |
| [61] | Zhu H, Chen X, Dai W, et al. Orientation Robust Object Detection in Aerial Images Using Deep Con-volutional Neural Network[C].IEEE International Conference on Image Processing, Quebec, Canada, 2015 |
| [62] | Yang Y, Newsam S. Bag-of-Visual-Words and Spatial Extensions for Land-use Classification[C]. International Conference on Advances in Geographic Information Systems, San Jose, California, 2010 |
| [63] | Kussul N, Lavreniuk M, Skakun S, et al. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5):778-782 |
| [64] | Wang L, Scott K A, Xu L, et al. Sea Ice Concentration Estimation During Melt from Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks:A Case Study[J].IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4524-4533 |
