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Received：21 June 2021，
Published：07 December 2023
移动端阅览
大数据驱动训练的深度学习模型是当今农作物分类的最新方法。当前研究仍然主要关注该模型方法的创新性，其在特定时间、特定地区的作物分类模型的泛化能力分析经常被忽略。因此，提高遥感分类模型在大尺度空间范围的有效迁移能力是遥感技术支撑地球系统科学研究和社会应用的关键。本研究通过设计实验分析了模型架构、作物物候特征、农区地块尺度、数据类型等因素对作物分类模型泛化能力的影响。结果表明：一方面当训练区和测试区地块大小发生明显变化时，MultiResUNet相对于SegNet，DeepLab V3+和U-Net具有更好的泛化性能。然而，单纯依靠MultiResUNet的泛化能力依然无法完全克服地块空间形态的变化对模型迁移的不利影响，为获得更高精度的华北玉米分布信息，需要优先使用与华北地区农业景观更相似的东北作物分布数据产品进行深度学习模型训练；另一方面，相对于TOA（Top of Atmosphere）数据，采用SR（Surface Reflectance）数据更有利于模型在跨洲际尺度进行空间迁移，因此，在大尺度作物制图研究中，应优先考虑使用SR数据。综上，本研究从一定程度上验证了影响农作物分类模型迁移性能的内在因素，可为大尺度作物制图提供科学依据。
Timely and accurate global crop mapping is important for global food security assessment. However
existing crop classification models are often targeted at specific regions
and their performance in other regions has not been fully evaluated. This study determined the critical period of crop growth in different regions to realize the effective transfer of the model in large-scale regions
and the remote sensing data during these critical periods were filtered such that the same crop in different regions showed similar characteristics on these remote sensing images. This way helps the model achieve a better transfer effect. In this study
the MultiResUNet
SegNet
DeepLab V3+
and U-Net models were trained using data from Northeast China
and the optimal F1 value for summer corn recognition in the study areas in North China can reach more than 0.97. This research also analyzed the factors that affected the generalization ability of the model. The issues addressed in this article include (1) using existing crop distribution data products as the ground truth samples for model training to solve the problem of lack of training samples for the deep learning model. We compared and analyzed the applicability of the models trained using the US Cropland Data Layer and Northeast crop distribution data products in North China. (2) We compared the generalization performance of depth models with different architectures. (3) We compared and analyzed the influence of different data types on the generalization ability of the model. (4) We comparatively analyzed the impact of crop phenology changes on the generalization ability of the model. Results show that MultiResUNet has better generalization performance than other networks when the plot size in the training and test areas varies significantly However
the generalization ability of MultiResUNet alone still cannot completely overcome the adverse effect of the change in plot spatial morphology on model migration. The crop distribution data products in Northeast China
which are more similar to the agricultural landscape in North China
need to be used for deep learning model training to obtain more accurate information of maize distribution in North China. Compared with TOA data
we found that SR data are more conducive to the spatial migration of the model at the transcontinental scale. Therefore
SR data should be given priority in large-scale crop mapping. This research provides a useful discussion for large-scale crop mapping research using only local samples.
Azzari G , Lobell D B . 2017 . Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring . Remote Sensing of Environment , 202 : 64 - 74 [ DOI: 10.1016/j.rse.2017.05.025 http://dx.doi.org/10.1016/j.rse.2017.05.025 ]
Boryan C , Yang Z W , Mueller R and Craig M . 2011 . Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program . Geocarto International , 26 ( 5 ): 341 - 358 [ DOI: 10.1080/10106049.2011.562309 http://dx.doi.org/10.1080/10106049.2011.562309 ]
Cai Y P , Guan K Y , Peng J , Wang S W , Seifert C , Wardlow B and Li Z . 2018 . A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach . Remote Sensing of Environment , 210 : 35 - 47 [ DOI: 10.1016/j.rse.2018.02.045 http://dx.doi.org/10.1016/j.rse.2018.02.045 ]
Chen L C , Papandreou G , Kokkinos I , Murphy K and Yuille A L . 2018a . DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs . IEEE Transactions on Pattern Analysis and Machine Intelligence , 40 ( 4 ): 834 - 848 [ DOI: 10.1109/TPAMI.2017.2699184 http://dx.doi.org/10.1109/TPAMI.2017.2699184 ]
Chen L C , Papandreou G , Schroff F and Adam H . 2017 . Rethinking atrous convolution for semantic image segmentation . arXiv preprint arXiv : 1706 . 05587 [ DOI: 10.48550/arXiv.1706.05587 http://dx.doi.org/10.48550/arXiv.1706.05587 ]
Chen L C , Zhu Y K , Papandreou G , Schroff F and Adam H . 2018b . Encoder-decoder with atrous separable convolution for semantic image segmentation // Proceedings of the 15th European Conference on Computer Vision . Munich : Springer: 833 - 851 [ DOI: 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49 ]
De Wit A J W and Clevers J G P W . 2004 . Efficiency and accuracy of per-field classification for operational crop mapping . International Journal of Remote Sensing , 25 ( 20 ): 4091 - 4112 [ DOI: 10.1080/01431160310001619580 http://dx.doi.org/10.1080/01431160310001619580 ]
Diao C Y . 2020 . Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages . Remote Sensing of Environment , 248 : 111960 [ DOI: 10.1016/j.rse.2020.111960 http://dx.doi.org/10.1016/j.rse.2020.111960 ]
Foody G M . 2020 . Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification . Remote Sensing of Environment , 239 : 111630 [ DOI: 10.1016/j.rse.2019.111630 http://dx.doi.org/10.1016/j.rse.2019.111630 ]
Fu S , Chen S H , Zhang X Q and Xu S Q . 2017 . Variations of precipitation and air temperature during 1960 - 2014 in Jiangsu province. Environmental Protection and Technology , 23 ( 2 ): 26 - 33
傅帅 , 陈舒慧 , 张小泉 , 徐士琦 . 2017 . 江苏省1960-2014年降水及气温变化特征 . 环保科技 , 23 ( 2 ): 26 - 33 [ DOI: 10.3969/j.issn.1674-0254.2017.02.006 http://dx.doi.org/10.3969/j.issn.1674-0254.2017.02.006 ]
Ge S , Zhang J S , Pan Y Z , Yang Z and Zhu S . 2021 . Transferable deep learning model based on the phenological matching principle for mapping crop extent . International Journal of Applied Earth Observation and Geoinformation , 102 : 102451 [ DOI: 10.1016/j.jag.2021.102451 http://dx.doi.org/10.1016/j.jag.2021.102451 ]
Hamdi Z M , Brandmeier M and Straub C . 2019 . Forest damage assessment using deep learning on high resolution remote sensing data . Remote Sensing , 11 ( 17 ): 1976 [ DOI: 10.3390/rs11171976 http://dx.doi.org/10.3390/rs11171976 ]
Hamrouni Y , Paillassa E , Chéret V , Monteil C and Sheeren D . 2021 . From local to global: a transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 . ISPRS Journal of Photogrammetry and Remote Sensing , 171 : 76 - 100 [ DOI: 10.1016/j.isprsjprs.2020.10.018 http://dx.doi.org/10.1016/j.isprsjprs.2020.10.018 ]
Hao P Y , Di L P , Zhang C and Guo L Y . 2020 . Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples . Science of the Total Environment , 733 : 138869 [ DOI: 10.1016/j.scitotenv.2020.138869 http://dx.doi.org/10.1016/j.scitotenv.2020.138869 ]
He K M , Zhang X Y , Ren S Q and Sun J . 2016 . Deep residual learning for image recognition // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Las Vegas : IEEE: 770 - 778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Ibtehaz N and Rahman M S . 2020 . MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation . Neural Networks , 121 : 74 - 87 [ DOI: 10.1016/j.neunet.2019.08.025 http://dx.doi.org/10.1016/j.neunet.2019.08.025 ]
Jiang Y L , Chen B W , Huang Y F , Cui J Q and Guo Y L . 2021 . Crop planting area extraction based on google earth engine and NDVI time series difference index . Journal of Geo-Information Science , 23 ( 5 ): 938 - 947
姜伊兰 , 陈保旺 , 黄玉芳 , 崔佳琪 , 郭宇龙 . 2021 . 基于Google Earth Engine和NDVI时序差异指数的作物种植区提取 . 地球信息科学学报 , 23 ( 5 ): 938 - 947 [ DOI: 10.12082/dqxxkx.2021.200291 http://dx.doi.org/10.12082/dqxxkx.2021.200291 ]
Kattenborn T , Eichel J , Wiser S , Burrows L , Fassnacht F E and Schmidtlein S . 2020 . Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery . Remote Sensing in Ecology and Conservation , 6 ( 4 ): 472 - 486 [ DOI: 10.1002/rse2.146 http://dx.doi.org/10.1002/rse2.146 ]
Kattenborn T , Leitloff J , Schiefer F and Hinz S . 2021 . Review on Convolutional Neural Networks (CNN) in vegetation remote sensing . ISPRS Journal of Photogrammetry and Remote Sensing , 173 : 24 - 49 [ DOI: 10.1016/j.isprsjprs.2020.12.010 http://dx.doi.org/10.1016/j.isprsjprs.2020.12.010 ]
Kingma D P and Ba J . 2014 . Adam: a method for stochastic optimization // 3rd International Conference on Learning Representations . San Diego : ICLR
Kluger D M , Wang S and Lobell D B . 2021 . Two shifts for crop mapping: leveraging aggregate crop statistics to improve satellite-based maps in new regions . Remote Sensing of Environment , 262 : 112488 [ DOI: 10.1016/j.rse.2021.112488 http://dx.doi.org/10.1016/j.rse.2021.112488 ]
Konduri V S , Kumar J , Hargrove W W , Hoffman F M and Ganguly A R . 2020 . Mapping crops within the growing season across the United States . Remote Sensing of Environment , 251 : 112048 [ DOI: 10.1016/j.rse.2020.112048 http://dx.doi.org/10.1016/j.rse.2020.112048 ]
LeCun Y , Bengio Y and Hinton G . 2015 . Deep learning . Nature , 521 ( 7553 ): 436 - 444 [ DOI: 10.1038/nature14539 http://dx.doi.org/10.1038/nature14539 ]
Li Q J , Liu J , Mi X F , Yang J and Yu T . 2021 . Object-oriented crop classification for GF-6 WFV remote sensing images based on Convolutional Neural Network . Journal of Remote Sensing , 25 ( 2 ): 549 - 558
李前景 , 刘珺 , 米晓飞 , 杨健 , 余涛 . 2021 . 面向对象与卷积神经网络模型的GF-6 WFV影像作物分类 . 遥感学报 , 25 ( 2 ): 549 - 558 [ DOI: 10.11834/jrs.20219347 http://dx.doi.org/10.11834/jrs.20219347 ]
Li Y H and Che S J . 2005 . Impacts of changes of temperature and precipitation on agriculture in Hebei regions . Chinese Journal of Agrometeorology , 26 ( 4 ): 224 - 228
李元华 , 车少静 . 2005 . 河北省温度和降水变化对农业的影响 . 中国农业气象 , 26 ( 4 ): 224 - 228 [ DOI: 10.3969/j.issn.1000-6362.2005.04.005 http://dx.doi.org/10.3969/j.issn.1000-6362.2005.04.005 ]
Li Z W , Shen H F , Cheng Q , Liu Y H , You S C and He Z Y . 2019 . Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors . ISPRS Journal of Photogrammetry and Remote Sensing , 150 : 197 - 212 [ DOI: 10.1016/j.isprsjprs.2019.02.017 http://dx.doi.org/10.1016/j.isprsjprs.2019.02.017 ]
Liu T , Abd-Elrahman A , Morton J and Wilhelm V L . 2018 . Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system . GIScience and Remote Sensing , 55 ( 2 ): 243 - 264 [ DOI: 10.1080/15481603.2018.1426091 http://dx.doi.org/10.1080/15481603.2018.1426091 ]
Neupane B , Horanont T and Hung N D . 2019 . Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV) . PLoS One , 14 ( 10 ): e 0223906 [ DOI: 10.1371/journal.pone.0223906 http://dx.doi.org/10.1371/journal.pone.0223906 ]
Nezami S , Khoramshahi E , Nevalainen O , Pölönen I and Honkavaara E . 2020 . Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks . Remote Sensing , 12 ( 7 ): 1070 [ DOI: 10.3390/rs12071070 http://dx.doi.org/10.3390/rs12071070 ]
Panboonyuen T , Jitkajornwanich K , Lawawirojwong S , Srestasathiern P and Vateekul P . 2017 . Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields . Remote Sensing , 9 ( 7 ): 680 [ DOI: 10.3390/rs9070680 http://dx.doi.org/10.3390/rs9070680 ]
Qi X M , Zhu P P , Wang Y B , Zhang L Q , Peng J H , Wu M F , Chen J L , Zhao X D , Zang N and Mathiopoulos P T . 2020 . MLRSNet: a multi-label high spatial resolution remote sensing dataset for semantic scene understanding . ISPRS Journal of Photogrammetry and Remote Sensing , 169 : 337 - 350 [ DOI: 10.1016/j.isprsjprs.2020.09.020 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.020 ]
Qi X N , Wang Y , Wang Q C , Liu Z Y and Bao Q . 2002 . The situation and developing prospect of corn zone in Jinlin province . Scientia Geographica Sinica , 22 ( 3 ): 379 - 384
齐晓宁 , 王洋 , 王其存 , 刘兆永 , 鲍强 . 2002 . 吉林玉米带的地位与发展前景 . 地理科学 , 22 ( 3 ): 379 - 384 [ DOI: 10.3969/j.issn.1000-0690.2002.03.022 http://dx.doi.org/10.3969/j.issn.1000-0690.2002.03.022 ]
Ronneberger O , Fischer P and Brox T . 2015 . U-Net: convolutional networks for biomedical image segmentation // Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich : Springer: 234 - 241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Rußwurm M and Körner M . 2017 . Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Honolulu : IEEE : 1496 - 1504 [ DOI: 10.1109/CVPRW.2017.193 http://dx.doi.org/10.1109/CVPRW.2017.193 ]
Schiefer F , Kattenborn T , Frick A , Frey J , Schall P , Koch B and Schmidtlein S . 2020 . Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks . ISPRS Journal of Photogrammetry and Remote Sensing , 170 : 205 - 215 [ DOI: 10.1016/j.isprsjprs.2020.10.015 http://dx.doi.org/10.1016/j.isprsjprs.2020.10.015 ]
Song C H , Woodcock C E , Seto K C , Lenney M P and Macomber S A . 2001 . Classification and change detection using landsat TM data: when and how to correct atmospheric effects? . Remote Sensing of Environment , 75 ( 2 ): 230 - 244 [ DOI: 10.1016/S0034-4257(00)00169-3 http://dx.doi.org/10.1016/S0034-4257(00)00169-3 ]
Song X P , Potapov P V , Krylov A , King L A , Di Bella C M , Hudson A , Khan A , Adusei B , Stehman S V and Hansen M C . 2017 . National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey . Remote Sensing of Environment , 190 : 383 - 395 [ DOI: 10.1016/j.rse.2017.01.008 http://dx.doi.org/10.1016/j.rse.2017.01.008 ]
Szegedy C , Liu W , Jia Y Q , Sermanet P , Reed S , Anguelov D , Erhan D , Vanhoucke V and Rabinovich A . 2015 . Going deeper with convolutions // 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Boston : IEEE: 1 - 9 [ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]
Wang S , Azzari G and Lobell D B . 2019 . Crop type mapping without field-level labels: random forest transfer and unsupervised clustering techniques . Remote Sensing of Environment , 222 : 303 - 317 [ DOI: 10.1016/j.rse.2018.12.026 http://dx.doi.org/10.1016/j.rse.2018.12.026 ]
Wang X , Zhang X C and Su C . 2020 . Land use classification of remote sensing images based on multi-scale learning and deep convolution neural . Journal of Zhejiang University (Science Edition) , 47 ( 6 ): 715 - 723
王协 , 章孝灿 , 苏程 . 2020 . 基于多尺度学习与深度卷积神经网络的遥感图像土地利用分类 . 浙江大学学报(理学版) , 47 ( 6 ): 715 - 723 [ DOI: 10.3785/j.issn.1008-9497.2020.06.009 http://dx.doi.org/10.3785/j.issn.1008-9497.2020.06.009 ]
Weinstein B G , Marconi S , Bohlman S A , Zare A and White E P . 2020 . Cross-site learning in deep learning RGB tree crown detection . Ecological Informatics , 56 : 101061 [ DOI: 10.1016/j.ecoinf.2020.101061 http://dx.doi.org/10.1016/j.ecoinf.2020.101061 ]
Wieland M , Li Y and Martinis S . 2019 . Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network . Remote Sensing of Environment , 230 : 111203 [ DOI: 10.1016/j.rse.2019.05.022 http://dx.doi.org/10.1016/j.rse.2019.05.022 ]
Xu C Y and Feng X Z . 2007 . Atmospheric correction on TM image and its influence analysis on spectral response characteristics . Journal of Nanjing University (Natural Sciences) , 43 ( 3 ): 309 - 317
徐春燕 , 冯学智 . 2007 . TM图像大气校正及其对地物光谱响应特征的影响分析 . 南京大学学报(自然科学) , 43 ( 3 ): 309 - 317
Xu J F , Zhu Y , Zhong R H , Lin Z X , Xu J L , Jiang H , Huang J F , Li H F and Lin T . 2020 . DeepCropMapping: a multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping . Remote Sensing of Environment , 247 : 111946 [ DOI: 10.1016/j.rse.2020.111946 http://dx.doi.org/10.1016/j.rse.2020.111946 ]
Yang R Y , Ahmed Z U , Schulthess U C , Kamal M and Rai R . 2020 . Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: a case study from Bangladesh . Remote Sensing Applications : Society and Environment , 20 : 100413 [ DOI: 10.1016/j.rsase.2020.100413 http://dx.doi.org/10.1016/j.rsase.2020.100413 ]
Yang S Q , Song Z S , Yin H P , Zhang Z T and Ning J F . 2021 . Crop classification method of UVA multispectral remote sensing based on deep semantic segmentation . Transactions of the Chinese Society for Agricultural Machinery , 52 ( 3 ): 185 - 192
杨蜀秦 , 宋志双 , 尹瀚平 , 张智韬 , 宁纪锋 . 2021 . 基于深度语义分割的无人机多光谱遥感作物分类方法 . 农业机械学报 , 52 ( 3 ): 185 - 192 [ DOI: 10.6041/j.issn.1000-1298.2021.03.020 http://dx.doi.org/10.6041/j.issn.1000-1298.2021.03.020 ]
You N S and Dong J W . 2020 . Examining earliest identifiable timing of crops using all available Sentinel 1 / 2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing , 161 : 109 - 123 [ DOI: 10.1016/j.isprsjprs.2020.01.001 http://dx.doi.org/10.1016/j.isprsjprs.2020.01.001 ]
You N S , Dong J W , Huang J X , Du G M , Zhang G L , He Y L , Yang T , Di Y Y and Xiao X M . 2021 . The 10-m crop type maps in Northeast China during 2017 — 2019 . Scientific Data , 8 ( 1 ): 41 [ DOI: 10.1038/s41597-021-00827-9 http://dx.doi.org/10.1038/s41597-021-00827-9 ]
Zhang D J , Pan Y Z , Zhang J S , Hu T G , Zhao J H , Li N and Chen Q . 2020 . A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution . Remote Sensing of Environment , 247 : 111912 [ DOI: 10.1016/j.rse.2020.111912 http://dx.doi.org/10.1016/j.rse.2020.111912 ]
Zhang L , Liu Z , Liu D Y , Xiong Q , Yang N , Ren T W , Zhang C , Zhang X D and Li S M . 2019 . Crop mapping based on historical samples and new training samples generation in Heilongjiang province, China . Sustainability , 11 ( 18 ): 5052 [ DOI: 10.3390/su11185052 http://dx.doi.org/10.3390/su11185052 ]
Zhang M , Lin H , Wang G X , Sun H and Fu J . 2018 . Mapping paddy rice using a convolutional neural network (CNN) with landsat 8 datasets in the Dongting lake area, China . Remote Sensing , 10 ( 11 ): 1840 [ DOI: 10.3390/rs10111840 http://dx.doi.org/10.3390/rs10111840 ]
Zhao H W , Chen Z X , Jiang H and Liu J . 2020 . Early growing stage crop species identification in southern China based on sentinel-1A time series imagery and one-dimensional CNN . Transactions of the Chinese Society of Agricultural Engineering , 36 ( 3 ): 169 - 177
赵红伟 , 陈仲新 , 姜浩 , 刘佳 . 2020 . 基于Sentinel-1A影像和一维CNN的中国南方生长季早期作物种类识别 . 农业工程学报 , 36 ( 3 ): 169 - 177 [ DOI: 10.11975/j.issn.1002-6819.2020.03.021 http://dx.doi.org/10.11975/j.issn.1002-6819.2020.03.021 ]
Zhong L H , Gong P and Biging G S . 2014 . Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery . Remote Sensing of Environment , 140 : 1 - 13 [ DOI: 10.1016/j.rse.2013.08.023 http://dx.doi.org/10.1016/j.rse.2013.08.023 ]
Zhong L H , Hu L N and Zhou H . 2019 . Deep learning based multi-temporal crop classification . Remote Sensing of Environment , 221 : 430 - 443 [ DOI: 10.1016/j.rse.2018.11.032 http://dx.doi.org/10.1016/j.rse.2018.11.032 ]
Zhong Y F , Hu X , Luo C , Wang X Y , Zhao J and Zhang L P . 2020 . WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H 2 ) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF . Remote Sensing of Environment , 250 : 112012 [ DOI: 10.1016/j.rse.2020.112012 http://dx.doi.org/10.1016/j.rse.2020.112012 ]
Zhu X X , Tuia D , Mou L C , Xia G S , Zhang L P , Xu F and Fraundorfer F . 2017 . Deep learning in remote sensing: a comprehensive review and list of resources . IEEE Geoscience and Remote Sensing Magazine , 5 ( 4 ): 8 - 36 [ DOI: 10.1109/MGRS.2017.2762307 http://dx.doi.org/10.1109/MGRS.2017.2762307 ]
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