Shun Ren, Xuan Liu, Hongyan Liu, and Lu Wang
[1] Z. Dong, M. Wang, and D.R. Li, A high resolution remotesensing image segmentation method by combining superpixelswith minimum spanning tree. Acta Geodaetica et Cartographicasinica, 46(6), 2017, 734–743. [2] D.W. Liu, L. Han, and X.Y. Han, High spatial resolutionremote sensing image classification based on deep learning,Acta Optica Sinica, 36(4), 2016, 1–9. [3] C. Liu, L. Hong, J, Chen, S. Chu, and M. Deng, Fusionof pixel-based and multi-scale region-based features for theclassification of high-resolution remote sensing image. Journalof Remote Sensing, 19(2), 2015, 228–239. [4] S.L. Lewisgonzales, N. Nagle, and K. Grace, Accuracy ofSupervised Classification of Cropland in Sub-Saharan Africa(Knoxville: University of Tennessee, 2015), 3386–3392. [5] X. Blaes, L. Vanhalle, and P. Defourny, Efficiency of cropidentification based on optical and SAR image time series,Remote Sensing of Environment, 96, 2005, 352–365. [6] Y. Mu, M.Q. Wu, and Z. Niu, Method of remote sensingextraction of cultivated land area under complex conditions insouthern region, Remote Sensing Technology and Application,35(5), 2020, 1127–1135 (in Chinese). [7] D. Liu, L. Han, and X.Y. Han, High spatial resolution remotesensing image classification based on deep learning, Acta OpticaSinica, 36(4), 2016, 4288–4289. [8] C. Liu, L. Hong, J. Chen, and M. Deng, Fusion of pixel-basedand multi-scale region-based features for the classificationof high-resolution remote sensing image, Journal of RemoteSensing, 19(2), 2015, 228–239. [9] J. Jin, Z.R. Zou, and C. Tao, Compressed text on based highresolution remote sensing image classification, Acta Geodaeticaet Cartographica Sinica, 43(5), 2014, 493—499. [10] Z.C. Wu, Z.W. Hu, Q. Zhang, and W. Cui, On combiningspectral, textural and shape features for remote sensing imagesegmentation, Acta Geodaetica et Cartographica sinica, 42(01),2013, 44–50. [11] L. Zhang, D. Ren, Z.Y. Huang, and S.M. Lei, Image stitch-ing method based on projective interpolation, InternationalJournal of Robotics and Automation, 31(5), 2016, 439–445. [12] S. Paisitkriangkrai, J. Sherrah, and P. Janney, Effffec-tive semantic pixel labelling with convolutional networksand conditional random fifields, IEEE, Computer Vision& Pattern Recognition Workshops, Boston, USA, 2015,36–43. [13] G. Papandreou, I. Kokkinos, and P.A. Savalle, Untangling localand global deformations in deep convolutional networks forimage classifification and sliding windowdetection, ComputerVision & Pattern Recognition Workshops,Boston, USA, 2015,1406–1419. [14] B. Vijay, K. Alex, and C. Roberto, SegNet: A deep convo-lutional encoder-decoder architecture for image segmentation,Transactions on Pattern Analysis and Machine Intelligence,39(12), 2017, 1109–1112. [15] F. Zhang, B. Du, and L. Zhang, Scene classificationvia a gradient boosting random convolutional networkframework, Geoscience and Remote Sensing, 54, 2016,1793–1803. [16] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutionalnetworks for biomedical image segmentation, InternationalConference on Medical Image Computing and Computer As-sisted Intervention, Springer International Publishing, Munich,Germany, 2015, 234–241. [17] L.C. Chen, G. Papandreou, I. Kokkinos, K. Mur-phy, and A.L. Yuille, Deeplab: Semantic image seg-mentation with deep convolutional nets, atrous convolu-tion, and fully connected CRFs, Transactions on Pat-tern Analysis and Machine Intelligence, 40(4), 2018,834–848. [18] J. Long, F. Shelhamer, and T. Darrell, Fully convolutionalnetworks for semantic segmentation, IEEE Transactions onPattern Analysis and Machine Intelligence, Boston, USA,39(4), 2015, 640–651. [19] S. Selim, I. Vladimir, B. Alexander, and S. Alexey, Featurepyramid network for multi-class land segmentation, Conferenceon Computer Vision and Pattern Recognition Workshops, 2018,272–275. [20] C. Tian, C. Li, and J. Shi, Dense fusion classmate networkfor land cover classification, Computer Vision and PatternRecognition, 2019, 192–196.
Important Links:
Go Back