High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set

Jun-hao ZUO; Cong ZHAO; Xiao-long ZHU; Hong-e REN

doi:10.3788/YJYXS20193404.0439

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High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set

Image Processing | 更新时间：2020-07-05

- High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 4, Pages: 439-447(2019)
- 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：10.3788/YJYXS20193404.0439
  CLC： TP751.1
- Received：14 October 2018，
  
  Accepted：22 January 2019，
  
  Published：2019-04
- 稿件说明：
移动端阅览
Jun-hao ZUO, Cong ZHAO, Xiao-long ZHU, et al. High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set[J]. Chinese journal of liquid crystals and displays, 2019, 34(4): 439-447.
DOI：

Jun-hao ZUO, Cong ZHAO, Xiao-long ZHU, et al. High-resolution remote sensing image building extraction combined with Faster-RCNN and Level-Set[J]. Chinese journal of liquid crystals and displays, 2019, 34(4): 439-447. DOI： 10.3788/YJYXS20193404.0439.

摘要

目前Level-Set图像分割方法存在初始轮廓的确定受人为因素影响较大的问题，对目标被遮盖和目标与背景灰度值相近无法达到理想的分割效果。针对此问题，本文提出了利用Faster-RCNN网络模型确定目标初始轮廓和区域信息的先验水平集图像分割方法，搭建Caffe深度学习框架训练Faster-RCNN网络模型；通过有监督学习的方式在IAILD数据集上训练模型，检测出目标建筑物并初步提取建筑物的轮廓，并将其与形状先验的Level-Set算法结合。对比实验结果表明，本文方法解决了Level-Set算法中图像分割结果初始轮廓受人为标记框选的影响较大的问题，能够更好地完成被遮挡建筑物的分割，对于目标建筑和背景灰度值相近也能达到更好的分割效果。

Abstract

At present

the Level-Set image segmentation method has the problem that the initial contour is greatly affected by human factors. The target segmentation and the target and background gray values are close to each other

and the ideal segmentation effect cannot be achieved. Aiming at this problem

this paper proposes a priori level set image segmentation method based on the Faster-RCNN network model to determine the initial contour and region information of the target

and builds the Caffe deep learning framework to train the Faster-RCNN network model. The IAILD data is obtained through supervised learning. The training model is assembled

the target building is detected and the outline of the building is initially extracted and combined with the shape-priority Level-Set algorithm. The experimental results show that the proposed method solves the problem that the initial contour of the image segmentation result is greatly influenced by the artificial marker frame selection in the Level-Set algorithm. It can better complete the segmentation of the occluded building

and achieve better segmentation effect for the target building and the background gray value.

关键词

Keywords

references

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