[1] 刘婧, 李培军. 结合结构和光谱特征的高分辨率影像分割方法[J]. 测绘学报, 2014, 43(5):466-473. DOI:10.13485/j.cnki.112089.2014.0087. LIU Jing, LI Peijun. A high resolution image segmentation method by combined structural and spectral characteristics[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5):466-473. DOI:10.13485/j.cnki.11-2089.2014.0087. [2] 周成虎, 骆剑承. 高分辨率卫星遥感影像地学计算[M]. 北京:科学出版社, 2009. ZHOU Chenghu, LUO Jiancheng. Geo-computing of high resolution satellite remote sensing image[M]. Beijing:Science Press, 2009. [3] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015:3431-3440. [4] WANG Hongzhen, WANG Ying, ZHANG Qian, et al. Gated convolutional neural network for semantic segmentation in high-resolution images[J]. Remote Sensing, 2017, 9(5):446. [5] GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, et al. A review on deep learning techniques applied to semantic segmentation[J]. arXiv:1704.06857, 2017. [6] HUBEL D H. The visual cortex of the brain[J]. Scientific American, 1963, 209:54-62. DOI:10.1038/scientificamerican1163-54. [7] KRÄHENBVHL P, KOLTUN V. Efficient inference in fully connected crfs with gaussian edge potentials[J]. arXiv:1210.5644, 2012:109-117. [8] SHOTTON J, WINN J, ROTHER C, et al. Textonboost for image understanding:multi-class object recognition and segmentation by jointly modeling texture, layout, and context[J]. International Journal of Computer Vision, 2009, 81(1):2-23. [9] DAI Jifeng, QI Haozhi, XIONG Yuwen, et al. Deformable convolutional networks[J]. arXiv:1703.06211, 2017:764-773. [10] LUO Wenjie, LI Yujia, URTASUN R, et al. Understanding the effective receptive field in deep convolutional neural networks[J]. arXiv:1701.04128, 2017. [11] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409.1556, 2014. [12] HOLSCHNEIDER M, KRONLAND-MARTINET R, MORLET J, et al. A real-time algorithm for signal analysis with the help of the wavelet transform[M]. COMBES J M, GROSSMANN A, TCHAMITCHIAN P. Wavelets:Time-Frequency Methods and Phase Space. Berlin:Springer, 1990:286-297. [13] DENG Jia, DONG Wei, SOCHER R, et al. Imagenet:a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA:IEEE, 2009. [14] DAI Jifeng, LI Yi, HE Kaiming, et al. R-FCN:object detection via region-based fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain:ACM, 2016:379-387. [15] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495. [16] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[C]//Proceedings of the International Conference on Learning Representations. San Diego, CA:Computational and Biological Learning Society, 2015. [17] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details:delving deep into convolutional nets[C]//Proceedings of the British Machine Vision Conference. Dundee, Britain:BMVA Press, 2014. [18] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409.1556, 2014. [19] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France:JMLR, 2015:448-456. [20] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers:surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1026-1034. [21] CLEVERT D A, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (ELUs)[C]//Proceedings of the International Conference on Learning Representations. San Diego, CA:Computational and Biological Learning Society, 2015. [22] EIGEN D, FERGUS R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:2650-2658. [23] MOSTAJABI M, YADOLLAHPOUR P, SHAKHNAROVICH G. Feedforward semantic segmentation with zoom-out features[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015:3376-3385. [24] ROTHER C, KOLMOGOROV V, BLAKE A. Grabcut:interactive foreground extraction using iterated graph cuts[C]//Proceedings of the ACM SIGGRAPH 2004. Los Angeles, California:ACM, 2004:309-314. [25] KOHLI P, LADICKY L, TORR P H S. Robust higher order potentials for enforcing label consistency[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA:IEEE, 2009. [26] KRÄHENBÜHL P, KOLTUN V. Efficient inference in fully connected CRFs with Gaussian edge potentials[C]//Proceedings of the 25th annual conference on Neural Information Processing Systems. Granada:NIF, 2011. [27] ADAMS A, BAEK J, DAVIS M A. Fast high-dimensional filtering using the permutohedral lattice[J]. Computer Graphics Forum, 2010,29(2):753-762. [28] GERKE M, ROTTENSTEINER F, WEGNER J D, et al. ISPRS semantic labeling contest[C]//Proceedings of PCV-Photogrammetric Computer Vision.[S.l.]:ISPRS, 2014. [29] GERKE M. Use of the stair vision library within the ISPRS 2D semantic labeling benchmark (Vaihingen)[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Boston, MA, USA:IEEE, 2015. [30] PAISITKRIANGKRAI S, SHERRAH J, JANNEY P, et al. Effective semantic pixel labelling with convolutional networks and conditional random fields[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA, USA:IEEE, 2015:36-43. [31] AUDEBERT N, LE SAUX B, LEFÈVRE S. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks[M]//LAI S H, LEPETIT V, NISHINO K, et al. Computer Vision-ACCV 2016. Cham:Springer, 2017. [32] ZHOU Hao, ZHANG Jun, LEI Jun, et al. Image semantic segmentation based on FCN-CRF model[C]//Proceedings of International Conference on Image, Vision and Computing. Portsmouth, UK:IEEE, 2016:9-14. |