A review for sample datasets of remote sensing imagery
- Vol. 26, Issue 4, Pages: 589-605(2022)
DOI： 10.11834/jrs.20221162
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摘要
随着机器学习、深度学习等人工智能技术在遥感领域的不断应用与发展，基于海量样本的数据驱动模型已经成为遥感影像信息提取的一种新的研究范式，其对样本数据的规模、质量、多样性等提出了更高要求。最近，国内外众多学者和研究机构相继发布了一系列遥感影像样本数据集，为大数据时代下遥感影像的信息提取和智能解译等奠定了研究基础。然而目前尚缺乏对上述影像样本数据集的综合分析，针对这一问题，本文在文献检索与分析的基础上，归纳总结了124个具有一定影响力且应用广泛的遥感影像样本数据集并对其元数据进行了分析，并提供了数据来源、应用领域与关键词的发展变化，分析了数据集在空间、时间、光谱分辨率上的差异，以应用领域为依据将其划分为场景识别、土地覆被/利用分类、专题要素提取、变化检测、目标检测、语义分割等8个类别并以部分数据为例进行了具体分析，总结了深度学习模型在数据集上的研究进展，并针对稀疏样本导致的模型过拟合问题，探讨了样本时空迁移、小样本和零样本学习、样本主动发现、样本生成等在遥感影像信息提取中的应用前景。本文首次对遥感影像样本数据集进行了综述研究，可为相关领域科研人员提供数据参考。
Abstract
With the rapid development of artificial intelligence technology such as machine learning and deep learning in remote sensing, data-driven models have become a new research paradigm for automatic information retrieval from remote sensing imagery, calling for higher requirements for the quantity, quality, and diversity of sample datasets. Before the era of deep learning, because classical machine learning methods (e.g., support vector machine and random forest) do not require huge numbers of samples for model training, the previously published sample datasets usually have a relatively small size (i.e., less than 100). In recent years, with the rapid development of technologies such as big data, parallel computing, and deep learning, many scholars and research institutions have issued a series of sample datasets, laying a solid foundation for a wide range of research and applications such as scene understanding, semantic segmentation, and object detection from remote sensing images. However, comprehensive review of the recently published sample datasets for remote sensing image analysis under the context of big data and deep learning remains lacking. Therefore, the objective of this study is to summarize and analyze these datasets to provide a valuable data reference for relevant researchers.,On the basis of literature retrieval and analysis, this paper summarized a total of 124 widely used, open access, and influential remote sensing image sample datasets that were published between 2001 and 2020.,We reviewed and summarized the development of recently published sample datasets for remote sensing imagery based on metadata analysis from the following aspects, such as data sources, application fields, keywords, and data size. Afterward, we analyzed these sample datasets from the perspective of spatial, spectral, and temporal resolutions. We listed the commonly used deep learning models (e.g., convolutional neural networks, recurrent neural networks, and generative adversarial networks) in the remote sensing field to show how these sample datasets could be used. We also divided the remote sensing image sample datasets into eight categories based on the following application fields: scene recognition, land cover/land use classification, thematic information extraction, change detection, ground-object detection, semantic segmentation, quantitative remote sensing, and other applications. The typical datasets and related research progress were carefully reviewed for each application field. In addition, because deep learning models are data-hungry, how to train a model with good generalization capability under limited labeled data has become a significant issue, especially for remote sensing applications given that obtaining sufficient labeled samples is time-consuming. To address this issue, we discussed several methods that could increase the model’s generalization capability, including sample transfer between spatio-temporal domains, few-shot learning, and zero-shot learning, active learning, and semi-supervised learning for sample discovery, as well as sample generation through generative adversarial networks.,By means of multi-dimensional analysis, we give a comprehensive overview of remote sensing image sample datasets. To the best of our knowledge, this paper is the first review of remote sensing image sample datasets for deep learning, potentially providing data reference for researchers in related fields.
关键词
遥感影像样本数据集机器学习深度学习
Keywords
remote sensing imagerysample datasetsmachine learningdeep learning
references
Bai J H, Xiao Q and Liu Q H. 2020. Ground observation data set from HL_RS_TS. Digital Journal of Global Change Data Repository.
柏军华, 肖青, 柳钦火. 2020. 怀来遥感站地面观测数据集. 全球变化数据仓储电子杂志. [DOI: 10.3974/geodb.2020.09.20.V1http://dx.doi.org/10.3974/geodb.2020.09.20.V1]
Bao J F. 2014. Research on Remote Sensing Image Processing with Machine Learning and Its Applications. Shanghai: Fudan University.
鲍江峰. 2014. 机器学习方法在遥感图像处理中的应用研究. 上海: 复旦大学.
Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M and Nemani R. 2015. DeepSat: a learning framework for satellite imagery//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington: ACM [DOI: 10.1145/2820783.2820816http://dx.doi.org/10.1145/2820783.2820816]
Benedek C and Szirányi T Á. 2009. Change detection in optical aerial images by a multilayer conditional mixed Markov model. IEEE Transactions on Geoscience and Remote Sensing, 47(10): 3416-3430 [DOI: 10.1109/tgrs.2009.2022633http://dx.doi.org/10.1109/tgrs.2009.2022633]
Bourdis N, Marraud D and Sahbi H. 2011. Constrained optical flow for aerial image change detection//2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada: IEEE [DOI: 10.1109/igarss.2011.6050150http://dx.doi.org/10.1109/igarss.2011.6050150]
Cen Y, Zhang L F, Zhang X, Wang Y M, Qi W C, Tang S L and Zhang P. 2020. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village). Journal of Remote Sensing, 24(11): 1299-1306
岑奕, 张立福, 张霞, 王跃明, 戚文超, 汤森林, 张鹏. 2020. 雄安新区马蹄湾村航空高光谱遥感影像分类数据集. 遥感学报, 24(11): 1299-1306 [DOI: 10.11834/jrs.20209065http://dx.doi.org/10.11834/jrs.20209065]
Chen Q, Wang L, Wu Y F, Wu G M, Guo Z L and Waslander S L. 2019. TEMPORARY REMOVAL: aerial Imagery for roof segmentation: a large-scale dataset towards automatic mapping of buildings. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 42-55 [DOI: 10.1016/j.isprsjprs.2018.11.011http://dx.doi.org/10.1016/j.isprsjprs.2018.11.011]
Cheng G and Han J W. 2016. A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 11-28 [DOI: 10.1016/j.isprsjprs.2016.03.014http://dx.doi.org/10.1016/j.isprsjprs.2016.03.014]
Chiu M T, Xu X Q, Wei Y C, Huang Z L, Schwing A G, Brunner R, Khachatrian H, Karapetyan H, Dozier I, Rose G, Wilson D, Tudor A, Hovakimyan N, Huang T S and Shi H H. 2020. Agriculture-vision: a large aerial image database for agricultural pattern analysis//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE [DOI: 10.1109/cvpr42600.2020.00290http://dx.doi.org/10.1109/cvpr42600.2020.00290]
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H and Bengio Y. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078
Cosmi Q and Globe D. 2018. SpaceNet on Amazon Web Services (AWS). “Datasets”: the SpaceNet catalog. Last modified October 1st [EB/OL]. https://spacenet.ai/datasets/https://spacenet.ai/datasets/
Dai D X and Yang W. 2011. Satellite image classification via two-layer sparse coding with biased image representation. IEEE Geoscience and Remote Sensing Letters, 8(1): 173-176 [DOI: 10.1109/lgrs.2010.2055033http://dx.doi.org/10.1109/lgrs.2010.2055033]
Daudt R C, Le Saux B, Boulch A and Gousseau Y. 2018. Urban change detection for multispectral earth observation using convolutional neural networks//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE [DOI: 10.1109/igarss.2018.8518015http://dx.doi.org/10.1109/igarss.2018.8518015]
Debes C, Merentitis A, Heremans R, HahnJ, Frangiadakis N, van Kasteren T, Liao W Z, Bellens R, Pižurica A, Gautama S, Gautama S, Philips W, Prasad S, Du Q and Pacifici F. 2014. Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2405-2418 [DOI: 10.1109/JSTARS.2014.2305441http://dx.doi.org/10.1109/JSTARS.2014.2305441]
Dierckx W, Sterckx S, Benhadj I, Livens S, Duhoux G, van Achteren T, Francois M, Mellab K and Saint G. 2014. PROBA-V mission for global vegetation monitoring: standard products and image quality. International Journal of Remote Sensing, 35(7): 2589-2614 [DOI: 10.1080/01431161.2014.883097http://dx.doi.org/10.1080/01431161.2014.883097]
Fujita A, Sakurada K, Imaizumi T, Ito R, Hikosaka S and Nakamura R. 2017. Damage detection from aerial images via convolutional neural networks//2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA). Nagoya, Japan: IEEE [DOI: 10.23919/mva.2017.7986759http://dx.doi.org/10.23919/mva.2017.7986759]
GIC. Hyperspectral Remote Sensing Scenes. 2020. Available online: http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_ Sensing_Scenes/http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes/[2021-10-01]
Hackel T, Savinov N, Ladicky L, Wegner J D, Schindler K and Pollefeys M. 2017. Semantic3d.net: a new large-scale point cloud classification benchmark. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1: 91-98 [DOI: 10.5194/isprs-annals-IV-1-W1-91-2017http://dx.doi.org/10.5194/isprs-annals-IV-1-W1-91-2017]
Han S and Han Q H. 2020. Review of semi-supervised learning research. Computer Engineering and Applications, 56(6): 19-27
韩嵩, 韩秋弘. 2020. 半监督学习研究的述评. 计算机工程与应用, 56(6): 19-27 [DOI: 10.3778/j.issn.1002-8331.1911-0083http://dx.doi.org/10.3778/j.issn.1002-8331.1911-0083]
Helber P, Bischke B, Dengel A and Borth D. 2019. EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7): 2217-2226 [DOI: 10.1109/jstars.2019.2918242http://dx.doi.org/10.1109/jstars.2019.2918242]
Hochreiter S and Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735-1780 [DOI: 10.1162/neco.1997.9.8.1735http://dx.doi.org/10.1162/neco.1997.9.8.1735]
Hughes M J and Hayes D J. 2014. Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing. Remote Sensing, 6(6): 4907-4926 [DOI: 10.3390/rs6064907http://dx.doi.org/10.3390/rs6064907]
Ji S P and Wei S Q. 2019. Building extraction via convolutional neural networks from an open remote sensing building dataset. Acta Geodaetica et Cartographica Sinica, 48(4): 448-459
季顺平, 魏世清. 2019. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法. 测绘学报, 48(4): 448-459 [DOI: 10.11947/j.AGCS.2019.20180206http://dx.doi.org/10.11947/j.AGCS.2019.20180206]
Johnson B and Xie Z X. 2013. Classifying a high resolution image of an urban area using super-object information. ISPRS Journal of Photogrammetry and Remote Sensing, 83: 40-49 [DOI: 10.1016/j.isprsjprs.2013.05.008http://dx.doi.org/10.1016/j.isprsjprs.2013.05.008]
Kaiser P, Wegner J D, Lucchi A, Jaggi M, Hofmann T and Schindler K. 2017. Learning aerial image segmentation from online maps. IEEE Transactions on Geoscience and Remote Sensing, 55(11): 6054-6068 [DOI: 10.1109/tgrs.2017.2719738http://dx.doi.org/10.1109/tgrs.2017.2719738]
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc.: 1097-1105
Lebedev M A, Vizilter Y V, Vygolov O V, Knyaz V A and Rubis A Y. 2018. Change detection in remote sensing images using conditional adversarial networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2: 565-571 [DOI: 10.5194/isprs-archives-XLII-2-565-2018http://dx.doi.org/10.5194/isprs-archives-XLII-2-565-2018]
Li K, Wan G, Cheng G, Meng L Q and Han J W. 2020. Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 159: 296-307 [DOI: 10.1016/j.isprsjprs.2019.11.023http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023]
Li W B and He R. 2020. Aircraft target detection of remote sensing images based on deep neural network. Computer Engineering, 46(7): 268-276
李文斌, 何冉. 2020. 基于深度神经网络的遥感图像飞机目标检测. 计算机工程, 46(7): 268-276 [DOI: 10.19678/j.issn.1000-3428.0057070http://dx.doi.org/10.19678/j.issn.1000-3428.0057070]
Li X, Li X W, Li Z Y, Wang J, Ma M G, Liu Q, Xiao Q, Hu Z Y, Che T, Wang J M, Liu Q H, Chen E X, Yan G J, Liu S M, Wang W Z, Zhang L X, Wang J D, Niu Z, Jin R, Ran Y H and Wang L X. 2012. Progresses on the watershed allied telemetry experimental research (WATER). Remote Sensing Technology and Application, 27(5): 637-649
李新, 李小文, 李增元, 王建, 马明国, 刘强, 肖青, 胡泽勇, 车涛, 王介民, 柳钦火, 陈尔学, 阎广建, 刘绍民, 王维真, 张立新, 王锦地, 牛铮, 晋锐, 冉有华, 王亮绪. 2012. 黑河综合遥感联合试验研究进展: 概述. 遥感技术与应用, 27(5): 637-649 [DOI: 10.11873/j.issn.1004-0323.2012.5.637http://dx.doi.org/10.11873/j.issn.1004-0323.2012.5.637]
Li X W. 2005. Retrospect, prospect and innovation in quantitative remote sensing. Journal of Henan University (Natural Science), 35(4): 49-56
李小文. 2005. 定量遥感的发展与创新. 河南大学学报(自然科学版), 35(4): 49-56 [DOI: 10.15991/j.cnki.411100.2005.04.012http://dx.doi.org/10.15991/j.cnki.411100.2005.04.012]
Long Y, Gong Y P, Xiao Z F and Liu Q. 2017. Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2486-2498 [DOI: 10.1109/tgrs.2016.2645610http://dx.doi.org/10.1109/tgrs.2016.2645610]
Lu L, Li X, Veroustraete F, Kang E and Wang J. 2009. Analysing the forcing mechanisms for net primary productivity changes in the Heihe River Basin, North‐West China. International Journal of Remote Sensing, 30(3): 793-816 [DOI: 10.1080/01431160802438530http://dx.doi.org/10.1080/01431160802438530]
Lu X Q, Wang B Q, Zheng X T and Li X L. 2017. Exploring models and data for remote sensing image caption generation. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2183-2195 [DOI: 10.1109/tgrs.2017.2776321http://dx.doi.org/10.1109/tgrs.2017.2776321]
Ma A L, Zhong Y F and Zhang L P. 2015. Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4202-4217 [DOI: 10.1109/tgrs.2015.2393357http://dx.doi.org/10.1109/tgrs.2015.2393357]
Maggiori E, Tarabalka Y, Charpiat G and Alliez P. 2017. Can semantic labeling methods generalize to any city? The inria aerial image labeling benchmark//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Fort Worth, TX, USA: IEEE [DOI: 10.1109/igarss.2017.8127684http://dx.doi.org/10.1109/igarss.2017.8127684]
Mnih V. 2013. Machine Learning for Aerial Image Labeling. Toronto: University of Toronto
Mou L C, Bruzzone L and Zhu X X. 2019. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 924-935 [DOI: 10.1109/tgrs.2018.2863224http://dx.doi.org/10.1109/tgrs.2018.2863224]
Mueller M, Smith N and Ghanem B. 2016. A benchmark and simulator for UAV tracking//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer [DOI: 10.1007/978-3-319-46448-0_27http://dx.doi.org/10.1007/978-3-319-46448-0_27]
Mundhenk T N, Konjevod G, Sakla W A and Boakye K. 2016. A large contextual dataset for classification, detection and counting of cars with deep learning//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer [DOI: 10.1007/978-3-319-46487-9_48http://dx.doi.org/10.1007/978-3-319-46487-9_48]
Papadomanolaki M, Vakalopoulou M and Karantzalos K. 2017. Patch-based deep learning architectures for sparse annotated very high resolution datasets//2017 Joint Urban Remote Sensing Event (JURSE). Dubai, United Arab Emirates: IEEE [DOI: 10.1109/jurse.2017.7924538http://dx.doi.org/10.1109/jurse.2017.7924538]
Petrişor A I. 2015. Assessment of the green infrastructure of Bucharest using Corine and urban atlas data. Urbanism Arhitectură Construcţii, 6(2): 19-24
Qiu Y B and Wang X X. 2020. Daily fractional snow cover dataset over High Asia during 2002 to 2018. National Cryosphere and Technology Data Center
邱玉宝, 王星星. 2020. 2002—2018年高亚洲逐日积雪覆盖度数据集. 国家冰川冻土沙漠科学数据中心 [DOI: 10.11922/sciencedb.457http://dx.doi.org/10.11922/sciencedb.457]
Rabbi J, Ray N, Schubert M, Chowdhury S and Chao D. 2020. Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network. Remote Sensing, 12(9): 1432 [DOI: 10.3390/rs12091432http://dx.doi.org/10.3390/rs12091432]
Robicquet A, Sadeghian A, Alexandre A and Savarese S. 2016. Learning social etiquette:Human trajectory understanding in crowded scenes//14th European conference on computer vision. Amsterdam, Netherlands: Springer: 549-565 [DOI:10.1007/978-3-319-46484-8_33http://dx.doi.org/10.1007/978-3-319-46484-8_33]
Schmitt M, Hughes L H and Zhu X X. 2018. The SEN1-2 dataset for deep learning in SAR-optical data fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1: 141-146 [DOI: 10.5194/isprs-annals-iv-1-141-2018http://dx.doi.org/10.5194/isprs-annals-iv-1-141-2018]
Settles B. 2009. Active Learning Literature Survey. Madison: University of Wisconsin: 52.
Sharma A, Liu X W and Yang X J. 2018. Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks. Neural Networks, 105: 346-355 [DOI: 10.1016/j.neunet.2018.05.019http://dx.doi.org/10.1016/j.neunet.2018.05.019]
Shermeyer J, Hossler T, Van Etten A, Hogan D, Lewis R and Kim D. 2020. RarePlanes: synthetic data takes flight//2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI, USA: IEEE [DOI: 10.1109/WACV48630.2021.00025http://dx.doi.org/10.1109/WACV48630.2021.00025]
Singh A. 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6): 989-1003 [DOI: 10.1080/01431168908903939http://dx.doi.org/10.1080/01431168908903939]
Sumbul G, Charfuelan M, Demir B and Markl V. 2019. Bigearthnet: a large-scale benchmark archive for remote sensing image understanding//2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE [DOI: 10.1109/igarss.2019.8900532http://dx.doi.org/10.1109/igarss.2019.8900532]
Tong X Y, Xia G S, Lu Q K, Shen H F, Li S Y, You S C and Zhang L P. 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237: 111322 [DOI: 10.1016/j.rse.2019.111322http://dx.doi.org/10.1016/j.rse.2019.111322]
Wang J S, Jiang N, Zhang G M, Li Y and Lv H. 2015. Incremental classification algorithm of hyperspectral remote sensing images based on spectral-spatial information. Acta Geodaetica et Cartographica Sinica, 44(9): 1003-1013
王俊淑, 江南, 张国明, 李杨, 吕恒. 2015. 融合光谱—空间信息的高光谱遥感影像增量分类算法. 测绘学报, 44(9): 1003-1013 [DOI: 10.11947/j.AGCS.2015.20140388http://dx.doi.org/10.11947/j.AGCS.2015.20140388]
Wang Q, Yuan Z H, Du Q and Li X L. 2019a. GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 3-13 [DOI: 10.1109/tgrs.2018.2849692http://dx.doi.org/10.1109/tgrs.2018.2849692]
Wang Y Q, Yao Q M, Kwok J T and Ni L M. 2020. Generalizing from a few examples: a survey on few-shot learning. ACM Computing Surveys, 53(3): 63 [DOI: 10.1145/3386252http://dx.doi.org/10.1145/3386252]
Wang Y Y, Wang C, Zhang H, Dong Y B and Wei S S. 2019b. A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sensing, 11(7): 765 [DOI: 10.3390/rs11070765http://dx.doi.org/10.3390/rs11070765]
Weber E and Kané H. 2020. Building disaster damage assessment in satellite imagery with multi-temporal fusion. arXiv: 2004.05525
Wei D S and Zhou X G. 2019. Automatic sampling of remote sensing image change detection samples based on prior information of vector data. Journal of Remote Sensing, 23(3): 464-475.
魏东升, 周晓光. 2019. 遥感影像变化检测样本自动抽样. 遥感学报, 23(3): 464-475[DOI：10.11834/jrs.20198013http://dx.doi.org/10.11834/jrs.20198013]
Wu C, Zhang L F and Zhang L P. 2016. A scene change detection framework for multi-temporal very high resolution remote sensing images. Signal Processing, 124: 184-197 [DOI: 10.1016/j.sigpro.2015.09.020http://dx.doi.org/10.1016/j.sigpro.2015.09.020]
Wu C, Zhang L P and Du B. 2017. Kernel slow feature analysis for scene change detection. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2367-2384 [DOI: 10.1109/tgrs.2016.2642125http://dx.doi.org/10.1109/tgrs.2016.2642125]
Xia G S, Bai X, Ding D, Zhu Z, Belongie S, Luo J B, Datcu M, Pelillo M and Zhang L P. 2018. DOTA: a large-scale dataset for object detection in aerial images//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE [DOI: 10.1109/cvpr.2018.00418http://dx.doi.org/10.1109/cvpr.2018.00418]
Xia G S, Hu J W, Hu F, Shi B G, Bai X, Zhong Y F, Zhang L P and Lu X Q. 2017. AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965-3981 [DOI: 10.1109/tgrs.2017.2685945http://dx.doi.org/10.1109/tgrs.2017.2685945]
Yan P Y. 2019. Research on Remote Sensing Image Scene Classification Based on Generative Adversarial Networks. Wuhan: Huazhong University of Science and Technology.
鄢佩瑶. 2019. 基于生成对抗网络的遥感图像场景分类研究. 武汉: 华中科技大学
Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, California: ACM [DOI: 10.1145/1869790.1869829http://dx.doi.org/10.1145/1869790.1869829]
Yang Y M. 2010. Estimation of evapotranspiration in Heihe River Basin based on remote sensing. Gansu: Lanzhou University
杨永民. 基于遥感的黑河流域蒸散发研究. 甘肃: 兰州大学, 2010
Yin J B, Gentine P, Zhou S, Sullivan S C, Wang R, Zhang Y and Guo S L. 2018. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nature Communications, 9(1): 4389 [DOI: 10.1038/s41467-018-06765-2http://dx.doi.org/10.1038/s41467-018-06765-2]
Yokoya N and Iwasaki A. 2016. Airborne Hyperspectral Data over Chikusei. Space Application Laboratory, University of Tokyo
Yu D H, Zhang B M, Zhao C, Guo H T and Lu J. 2020. Scene classification of remote sensing image using ensemble convolutional neural network. Journal of Remote Sensing, 24(6): 717-727
余东行, 张保明, 赵传, 郭海涛, 卢俊. 2020. 联合卷积神经网络与集成学习的遥感影像场景分类. 遥感学报, 24(6): 717-727 [DOI: 10.11834/jrs.20208273http://dx.doi.org/10.11834/jrs.20208273]
Yu L X, Zhang S W, Bu K, Yang J C, Yan F Q and Chang L P. 2013. A Review on Snow Data Sets. Scientia Geographica Sinica, 33(7): 878-883
于灵雪, 张树文, 卜坤, 杨久春, 颜凤芹, 常丽萍. 2013. 雪数据集研究综述. 地理科学, 33(7): 878-883 [DOI: 10.13249/j.cnki.sgs.2013.07.015http://dx.doi.org/10.13249/j.cnki.sgs.2013.07.015]
Yu X F, Zhuang D F and Wang Q K. 2014. Remote sensing based forest phenology data of Northeast China (2000-2010). Acta Geographica Sinica, 69(S1): 72-76
于信芳, 庄大方, 王乾坤. 2014. 中国东北森林物候期遥感监测数据集. 地理学报, 69(S1): 72-76 [DOI: 10.11821/dlxb2014S014http://dx.doi.org/10.11821/dlxb2014S014]
Yuan J Y, Gleason S S and Cheriyadat A M. 2013. Systematic benchmarking of aerial image segmentation. IEEE Geoscience and Remote Sensing Letters, 10(6): 1527-1531 [DOI: 10.1109/lgrs.2013.2261453http://dx.doi.org/10.1109/lgrs.2013.2261453]
Zeng Y L, Li J and Liu Q H. 2012. Global LAI ground validation dataset and product validation framework: a review. Advances in Earth Science, 27(2): 165-174
曾也鲁, 李静, 柳钦火. 2012. 全球LAI地面验证方法及验证数据综述. 地球科学进展, 27(2): 165-174 [DOI: 10.11867/j.issn.1001-8166.2012.02.0165http://dx.doi.org/10.11867/j.issn.1001-8166.2012.02.0165]
Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
张兵. 2018. 遥感大数据时代与智能信息提取. 武汉大学学报(信息科学版), 43(12): 1861-1871 [DOI: 10.13203/j.whugis20180172http://dx.doi.org/10.13203/j.whugis20180172]
Zhang L N and Zhou Q. 2014. Proceedings of the Twenty-eighth Academic Information Exchange Meeting of Central South Branch of National Surveying and Mapping Science and Technology Information Network. Kaifeng: Science and Technology Information Network Branch of China Society of Surveying and Mapping
张丽娜, 周强. 2014. 遥感影像解译样本采集关键技术分析//全国测绘科技信息网中南分网第二十八次学术信息交流会论文集. 开封: 中国测绘学会科技信息网分会
Zhang M, Hu X Y, Zhao L K, Lv Y, Luo M and Pang S Y. 2017. Learning dual multi-scale manifold ranking for semantic segmentation of high-resolution images. Remote Sensing, 9(5):500 [DOI:10.3390/rs9050500http://dx.doi.org/10.3390/rs9050500]
Zhang X R, Liu L P, Fang S, Jiang W C and Wang J C. 2014. Research advances on the relationship between land use/cover change and environmental change. Ecology and Environmental Sciences, 23(12): 2013-2021
张新荣, 刘林萍, 方石, 姜文超, 王金臣. 2014. 土地利用、覆被变化(LUCC)与环境变化关系研究进展. 生态环境学报, 23(12): 2013-2021 [DOI: 10.16258/j.cnki.1674-5906.2014.12.018http://dx.doi.org/10.16258/j.cnki.1674-5906.2014.12.018]
Zhang Y L, Yuan Y, Feng Y C and Lu X Q. 2019. Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5535-5548 [DOI: 10.1109/tgrs.2019.2900302http://dx.doi.org/10.1109/tgrs.2019.2900302]
Zhu H G, Chen X G, Dai W Q, Fu K, Ye Q X and Jiao J B. 2015. Orientation robust object detection in aerial images using deep convolutional neural network//2015 IEEE International Conference on Image Processing (ICIP). Quebec City, QC, Canada: IEEE [DOI: 10.1109/icip.2015.7351502http://dx.doi.org/10.1109/icip.2015.7351502]
Zhu P F, Wen L Y, Du D W, Bian X, Fan H, Hu Q H and Ling H B. 2020. Detection and tracking meet drones challenge. arXiv: 2001.06303
Zhu Q Q, Zhong Y F, Zhao B, Xia G S and Zhang L P. 2016. Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 13(6): 747-751 [DOI: 10.1109/lgrs.2015.2513443http://dx.doi.org/10.1109/lgrs.2015.2513443]
Zou Q, Ni L H, Zhang T and Wang Q. 2015. Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12(11): 2321-2325 [DOI: 10.1109/lgrs.2015.2475299http://dx.doi.org/10.1109/lgrs.2015.2475299]
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