Remote sensing image semantic segmentation combining UNET and FPN

Xi WANG; Ming YU; Hong-e REN

doi:10.37188/CJLCD.2020-0116

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Remote sensing image semantic segmentation combining UNET and FPN

Image Processing | 更新时间：2021-03-23

- Remote sensing image semantic segmentation combining UNET and FPN
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 3, Pages: 475-483(2021)
- 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：
- 基金信息：
- DOI：10.37188/CJLCD.2020-0116
  CLC： TP751.1
- Received：07 August 2020，
  
  Accepted：04 September 2020，
  
  Published：2021-03
- 稿件说明：
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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：

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

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