UNET与FPN相结合的遥感图像语义分割

王曦; 于鸣; 任洪娥

doi:10.37188/CJLCD.2020-0116

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UNET与FPN相结合的遥感图像语义分割

图像处理 | 更新时间：2021-03-23

- UNET与FPN相结合的遥感图像语义分割
- Remote sensing image semantic segmentation combining UNET and FPN
- 液晶与显示 2021年36卷第3期页码：475-483
- 作者机构：
  
  1.东北林业大学信息与计算机工程学院, 黑龙江哈尔滨 150040
  2.黑龙江省林业智能装备工程研究中心, 黑龙江哈尔滨 150040
- 作者简介：
  
  [ "王曦(1995-), 女, 黑龙江哈尔滨人, 硕士研究生, 2017年于哈尔滨师范大学获得学士学位, 主要从事图像识别与智能控制方面的研究。E-mail:qq0224wx@163.com" ]
  [ "于鸣(1981-), 男, 黑龙江哈尔滨人, 博士研究生, 讲师, 2006年于东北林业大学获得硕士学位, 主要从事图像识别与智能控制方面的研究。E-mail: yuming@nefu.edu.cn" ]
  [ "任洪娥(1962-), 女, 吉林白山人, 博士, 教授, 2009年于东北林业大学获得博士学位, 主要从事图像识别与智能控制方面的研究。E-mail: nefu_rhe@163.com" ]
- 基金信息：
  
  中央高校基本科研业务费专项资金资助项目(2572017PZ10)
- DOI：10.37188/CJLCD.2020-0116
  中图分类号： TP751.1
- 收稿日期：2020-08-07，
  
  录用日期：2020-9-4，
  
  纸质出版日期：2021-03
- 稿件说明：
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王曦, 于鸣, 任洪娥. UNET与FPN相结合的遥感图像语义分割[J]. 液晶与显示, 2021,36(3):475-483.

Xi WANG, Ming YU, Hong-e REN. Remote sensing image semantic segmentation combining UNET and FPN[J]. Chinese journal of liquid crystals and displays, 2021, 36(3): 475-483.
王曦, 于鸣, 任洪娥. UNET与FPN相结合的遥感图像语义分割[J]. 液晶与显示, 2021,36(3):475-483. DOI： 10.37188/CJLCD.2020-0116.

Xi WANG, Ming YU, Hong-e REN. Remote sensing image semantic segmentation combining UNET and FPN[J]. Chinese journal of liquid crystals and displays, 2021, 36(3): 475-483. DOI： 10.37188/CJLCD.2020-0116.

摘要

针对传统的遥感图像分割方法效率低下，复杂场景下分割精细度不够，以及UNET模型对于图像中包含的较小目标以及较大目标的边缘分割效果不佳等问题，本文提出了一种UNET结构与FPN结构相结合的方法，提升UNET模型整合多尺度信息的能力，同时辅以能更好地捕捉目标边缘的BLR损失函数，提升UNET模型对目标边界的分割效果。实验结果表明，本文所使用的方法有效提升了语义分割的精度，较好地缓解了对小尺度目标和大尺度目标边缘分割不佳的问题。该方法对目标边缘分割更精准，达到更好的分割效果。

Abstract

The traditional remote sensing image segmentation method is inefficient and the segmentation fineness is not enough in complex scenes. The UNET model is well-known for its good segmentation effect

but it does not perform well for the smaller objects contained in the image and the edge segmentation of larger objects. In order to solve this problem

a method combining UNET structure with FPN structure is proposed in this paper to improve the ability of UNET model to integrate multi-scale information. At the same time

the BLR loss function which can better capture the edge of the target edge is used to improve the segmentation effect of UNET model on the target boundary. The experimental results show that the method used in this paper effectively improves the accuracy of semantic segmentation and alleviates the problem of poor edge segmentation of small-scale targets and large-scale targets. The target edge segmentation can be more accurate to achieve better segmentation results.

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references

LECUN Y, BOTTOU L, BENGIO Y, et al . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE , 1998, 86(11): 2278-2324.

REN S Q, HE K M, GIRSHICK R, et al . Faster R-CNN: towards real-time object detection with region proposal networks[C]// Proceedings of the 28 th International Conference on Neural Information Processing Systems . Montreal, Canada: NIPS, 2015: 91-99.

曹玉良, 明廷锋, 贺国, 等. 基于深度学习的离心泵空化状态识别[J]. 西安交通大学学报, 2017, 51(11): 165-172.

CAO Y L, MING T F, HE G, et al . Cavitation state recognition of centrifugal pump based on deep learning[J]. Journal of Xi'an Jiaotong University , 2017, 51(11): 165-172. (in Chinese).

FARFADE S S, SABERIAN M J, LI L J. Multi-view face detection using deep convolutional neural networks[C]// Proceedings of the 5 th ACM on International Conference on Multimedia Retrieval . Shanghai: ACM, 2015: 643-650.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25 th International Conference on Neural Information Processing Systems . Lake Tahoe, USA: ACM, 2012: 1097-1105.

周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.

ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network[J]. Chinese Journal of Computers , 2017, 40(6): 1229-1251. (in Chinese)

SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017, 39(4): 640-651.

LU Y, CHEN Y R, ZHAO D B, et al . Graph-FCN for image semantic segmentation[C]// Proceedings of the 16 th International Symposium on Neural Networks . Moscow, Russia: Springer, 2020: 97-105.

周锐烨, 沈文忠. PI-Unet: 异质虹膜精确分割神经网络模型的研究[J/OL]. 计算机工程与应用. (2020-08-06)[2020-09-04] . https://www.cnki.net/KCMS/detail/11.2127.TP.20200806.1353.016.html https://www.cnki.net/KCMS/detail/11.2127.TP.20200806.1353.016.html .

ZHOU R Y, SHEN W Z. PI-Unet: research on a neural network model for accurate segmentation of heterogeneous iris[J/OL]. Computer Engineering and Applications . (2020-08-06)[2020-09-04]. https://www.cnki.net/KCMS/detail/11.2127.TP.20200806.1353.016.html https://www.cnki.net/KCMS/detail/11.2127.TP.20200806.1353.016.html .

李忠智, 尹航, 左剑凯, 等. 基于UNet++与多边输出融合算法的船舶检测方法[J/OL]. 计算机工程. (2020-08-07)[2020-09-04] . https://www.cnki.net/KCMS/detail/31.1289.TP.20200806.1722.004.html https://www.cnki.net/KCMS/detail/31.1289.TP.20200806.1722.004.html .

LI Z Z, YIN H, ZUO J K, et al . Ship detection method based on UNet++ and multilateral output fusion algorithm[J/OL]. Computer Engineering . (2020-08-07)[2020-09-04]. https://www.cnki.net/KCMS/detail/31.1289.TP.20200806.1722.004.html https://www.cnki.net/KCMS/detail/31.1289.TP.20200806.1722.004.html .

蒋宏达, 叶西宁. 一种改进的I-Unet网络的皮肤病图像分割算法[J]. 现代电子技术, 2019, 42(12): 52-56.

JIANG H D, YE X N. An improved skin disease image segmentation algorithm based on I-Unet network[J]. Modern Electronics Technique , 2019, 42(12): 52-56. (in Chinese)

CHEN X L, GIRSHICK R, HE K M, et al . Tensormask: a foundation for dense object segmentation[C]// Proceedings of the IEEE / CVF International Conference on Computer Vision . Seoul, Korea (South): IEEE, 2019: 2061-2069.

梁华, 宋玉龙, 钱锋, 等. 基于深度学习的航空对地小目标检测[J]. 液晶与显示, 2018, 33(9): 793-800.

LIANG H, SONG Y L, QIAN F, et al . Detection of small target in aerial photography based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays , 2018, 33(9): 793-800. (in Chinese)

陈彦彤, 李雨阳, 吕石立, 等. 基于深度语义分割的多源遥感图像海面溢油监测[J]. 光学精密工程, 2020, 28(5): 1165-1176.

CHEN Y T, LI Y Y, LÜ S L, et al . Research on oil spill monitoring of multi-source remote sensing image based on deep semantic segmentation[J]. Optics and Precision Engineering , 2020, 28(5): 1165-1176. (in Chinese)

ZHU Y, SAPRA K, REDA F A, et al . Improving semantic segmentation via video propagation and label relaxation[C]// Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition . Long Beach, CA, USA: IEEE, 2019: 8848-8857.

CAI Z W, VASCONCELOS N. Cascade R-CNN: high quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019, doi:10.1109/TPAMI.2019.2956516.

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.

CHEN L C, PAPANDREOU G, KOKKINOS I, et al . Semantic image segmentation with deep convolutional nets and fully connected CRFs[C]// Proceedings of International Conference on Learning Representations . San Diego, United States, 2015.

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 , 2018, 40(4): 834-848.

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