Remote Sensing Image Segmentation Method Based on Multi-Level Channel Attention

Shuai Yu; Xili Wang

doi:10.3788/LOP57.041012

[1] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 3431-3440(2015).

[2] Chen L C, Papandreou 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 and Machine Intelligence, 40, 834-848(2018).

[3] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation. [C]∥2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA. New York: IEEE, 1451-1460(2018).

[4] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5168-5177(2017).

[5] Liu F, Liu P Y, Zhang J N et al. Joint detection of RGB-D images based on double flow convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 021503(2018).

[6] He Q, Li Y, Song W et al. Multimodal remote sensing image classification with small sample size based on high-level feature fusion[J]. Laser & Optoelectronics Progress, 56, 111001(2019).

[7] Cao C S, Liu X M, Yang Y et al. Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2956-2964(2015).

[8] Wang F, Jiang M Q, Qian C et al. Residual attention network for image classification. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6450-6458(2017).

[9] Fu J, Liu J, Tian H J et al. -04-21)[2019-06-21]. https:∥arxiv., org/abs/1809, 02983(2019).

[10] IoffeS, SzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥International Conference on Machine Learning, July 6-11, 2015, Lille, France. USA: MIT Press, 2015: 448- 456.

[11] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. [C]∥Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. [S.l.: s.n.], 807-814(2010).

[12] Mnih V. Machine learning for aerial image labeling[D]. Canada: University of Toronto, 20-41(2013).

[13] Maggiori E, Tarabalka Y, Charpiat G et al. Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. [C]∥2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 23-28, 2017, Fort Worth, TX, USA. New York: IEEE, 3226-3229(2017).

[14] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science. Cham: Springer, 9351, 234-241(2015).

[15] Zhong Z L, Li J, Cui W H et al. Fully convolutional networks for building and road extraction: preliminary results. [C]∥2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China. New York: IEEE, 1591-1594(2016).

[16] Muruganandham S. Semantic segmentation of satellite images using deep learning[D]. Lulea: Lulea University of Technology(2016).

[17] Panboonyuen T, Jitkajornwanich K, Lawawirojwong S et al. Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields[J]. Remote Sensing, 9, 680(2017).

[18] Maggiori E, Tarabalka Y, Charpiat G et al. High-resolution aerial image labeling with convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 7092-7103(2017).

[19] He K M, Gkioxari G, Dollár P et al. Mask R-CNN. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2980-2988(2017).

[20] Mou L C. -05-05)[2019-06-21]. https:∥arxiv.gg363., site/abs/1805, 02091(2018).