Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0628002(2023)
DSNet-Based Remote Sensing Image Semantic Segmentation Method
[1] Xu H, Zhu Y H, Zhen T et al. Survey of image semantic segmentation methods based on deep neural network[J]. Journal of Frontiers of Computer Science and Technology, 15, 47-59(2021).
[2] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).
[3] 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, 39, 2481-2495(2017).
[5] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C], 5168-5177(2017).
[6] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).
[7] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C], 3141-3149(2019).
[8] Liang X D, Shen X H, Feng J S et al. Semantic object parsing with graph LSTM[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 125-143(2016).
[10] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).
[12] Liu S W, Cui Z Y, Li D Y. Multi-task learning for building object semantic segmentation of remote sensing image based on UNet network[J]. Remote Sensing for Land & Resources, 32, 74-83(2020).
[13] Shen Y S, Wang A C. Remote sensing image feature segmentation method based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays, 36, 733-740(2021).
[14] Zhou Z W, Siddiquee M M R, Tajbakhsh N et al. UNet++: redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 39, 1856-1867(2020).
[15] Yuan W, Zhou T, Xi Z S et al. MUNet: a multi-branch adaptive deep learning network for remote sensing image semantic segmentation[J]. Journal of Geomatics Science and Technology, 37, 581-588(2020).
[16] Zhang Y H, Xi M D. A method for extracting buildings from remote sensing images with hole U-Net++ network[J]. Journal of Geomatics, 46, 82-86(2021).
[17] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).
[18] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).
[19] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).
Get Citation
Copy Citation Text
Fangxing Shi, line Zhou, Daming Zhu, Zhitao Fu. DSNet-Based Remote Sensing Image Semantic Segmentation Method[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0628002
Category: Remote Sensing and Sensors
Received: Nov. 8, 2021
Accepted: Jan. 7, 2022
Published Online: Mar. 16, 2023
The Author Email: Daming Zhu (634617255@qq.com)