Computer Science ›› 2021, Vol. 48 ›› Issue (8): 162-168.doi: 10.11896/jsjkx.200700182
• Computer Graphics & Multimedia • Previous Articles Next Articles
WANG Shi-yun, YANG Fan
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[1]WANG B,FAN D L.A Summary of the Research Progress of Deep Learning in Remote Sensing Image Classification and Re-cognition[J].Bulletin of Surveying and Mapping,2019,503(2):108-111,145. [2]QIN Y Q,CHI M M.High-resolution remote sensing image semantic segmentation method combined with scene classification data[J].Computer Applications and Software,2020,37(06):126-129,134. [3]WANG E D,QI K,LI X P,et al.Semantic segmentation method of remote sensing image based on neural network[J].Acta Optica Sinica,2019,39(12):93-104. [4]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//The IEEE Conference on Computer Vision and Pattern Recognition.Boston,USA,2015:3431-3440. [5]YU F,KOLTUN V.Multi-Scale Context Aggregation by Dila-ted Convolutions[C]//International Conference on Learning Representations.San Juan,Puerto Rico,2016. [6]CHEN L C,PAPANDEROU 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 & Machine Intelligence,2016,40(4):834-848. [7]RONNEBERGER O,FISCHER P,BROX T,et al.U-net:Con-volutional networks for biomedical image segmentation[J].Medical Image Computing and Computer Assisted Intervention,2015,28(4):234-241. [8]YUAN J Y.Automatic building extraction in aerial scenes using convolutional networks[J].arXiv:1602.06564,2016. [9]SU J M,YANG L X,JING W P.Semantic segmentation method of high-resolution remote sensing image based on U-Net[J].Computer Engineering and Applications,2019,55(7):207-213. [10]BERMAN M,TRIKI A R,BLASCHKO M B.The Lovász-softmax loss:a tractable surrogate for the optimization of the intersection-over-union measure in neural networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:UT,2018:4413-4421. [11]SHI W Z,CABALLERO J,HUSZAR F,et al.Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas,NV,2016:1874-1883. [12]MAGGIORI E,TARABALKA Y,CHARPIAT G,et al.High-resolution aerial image labeling with convolutional neural networks[C]//IEEE Transactions on Geoscience and Remote Sensing.2017:7092-7103. [13]ZHOU J Y,ZHAO Y M.Overview of Convolutiotnal NeuralNetworks in Image Classification and Target Detection[J].Computer Engineering and Applications,2017,53(13):34-41. [14]PASCANU R,MIKOLOV T,BENGIO Y.On the difficulty of training recurrent neural networks[C]//Proceedings of the 30th International Conference on Machine Learning(CML2013).Atlanta,GA,USA,2013:1310-1318. [15]IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167v3,2015. [16]XU Z J,YANG X B,HE L M,et al.Multiscale remote sensing semantic segmentation network[J/OL].Computer Engineering and Applications:1-9[2020-07-18].http://kns.cnki.net/kcms/detail/11.2127.TP.20200423.1009.006.html. |
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