结合DCNN与短距条件随机场的遥感影像道路提取

赫晓慧; 陈明扬; 李盼乐; 田智慧; 周广胜

doi:10.13203/j.whugis20210464

结合DCNN与短距条件随机场的遥感影像道路提取

doi: 10.13203/j.whugis20210464

1. 郑州大学, 地球科学与技术学院, 河南郑州, 450052;
2. 郑州大学, 信息工程学院, 河南郑州, 450052;
3. 中国气象科学研究院郑州大学生态气象联合实验室郑州, 450052

基金项目:

第二次青藏高原综合考察研究项目（2019QZKK0106）

详细信息

作者简介:
赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

中图分类号: TP75

Road Extraction from Remote Sensing Image by Integrating DCNN with Short Range Conditional Random Field(SRCRF)

1. School of Earth Science and Technology, Zhengzhou University, Zhengzhou 450052, China;
2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China;
3. Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450052

Funds:

The Second Comprehensive Investigation and Research Project on Qinghai-Tibet Plateau.(2019QZKK0106)

摘要: 深度卷积神经网络（Deep Convolutional Neural Network，DCNN）在高分辨率遥感图像（High-Resolution Remote Sensing images，HRSIs）自动道路提取领域被广泛应用，但现有方法难以对预测结果中像素间的上下文关系建模。针对此问题，已有研究利用全连接条件随机场(Fully Connected Crf ，FullCrf）结合上下文信息对语义分割结果进行二次优化，但无法有效改善道路结构不连续问题。为改善道路结构的完整性，本研究提出一种结合DCNN的短距条件随机场模型（Short Range Conditional Random Filed，SRCRF），SRCRF利用DCNN强大的特征提取能力并控制FullCrf的推理范围缓解过渡平滑现象，解决提道路取结果中的结构不连续、不完整问题。实验结果表明，在Zimbawe-Roads数据集与Cheng-Roads数据集中SRCRF的F1值相比DCNN分别上升大约4.01%与3.73%，相比FullCrf分别上升大约3.25%和2.28%。
- 遥感影像 /
- 道路提取 /
- 深度学习 /
- 条件随机场 /
- 语义分割
Abstract: Objective: Deep Convolutional Neural Network (DCNN) is widely used in automatic road extraction from high-resolution Remote Sensing images (HRSIs). However, the existing methods are difficult to model the context relationship between pixels in the predicted results. To solve this problem, some studies have used Fully Connected Crf (FullCrf) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. In order to improve the integrity of road structure, this study proposes a Short Range Conditional Random Filed (SRCRF) model combined with DCNN. Methods: SRCRF mainly includes unary potential function based on road pre-segmentation, binary potential function based on spectral spatial features and k-neighborhood mean field inference algorithm. Firstly, the priori knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF, Secondly, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency that is, adjacent pixels with similar spectral features have the same label. Finally, K-neighborhood mean field inference algorithm based on mean field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors (road-road). Results: The experimental results show that SRCRF alleviates the transition smoothness of FullCrf, and alleviates the structural discontinuity and incompleteness in the road acquisition results of high resolution remote sensing images. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1 values of SRCRF increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCrf. Conclusion: Compared with traditional deep learning methods for road extraction from high resolution remote sensing images, this paper proposes a new road extraction scheme for remote sensing images, SRCRF. This scheme combines the advantages of DCNN and optimizes the fully connected structure of traditional conditional random fields into k-neighborhood structure, which reduces the inference scope and improves the proportion of feature vectors (road to road). Compared with FullCrf, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. According to the results of SRCRF on Cheng-Roads and Zimbawe-Roads data sets, the performance of this method is improved compared with DCNN and FullCrf, and the time is shortened by one order of magnitude compared with FullCrf. In future work, we will further investigate the potential for learning Gaussian features and investigate more complex CRF architectures to better capture global context information. Finally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc.
- Remote sensing image /
- Road extraction /
- Deep learning /
- Conditional random field /
- Semantic segmentation

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留言板

结合DCNN与短距条件随机场的遥感影像道路提取

doi: 10.13203/j.whugis20210464

作者简介:
赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

Road Extraction from Remote Sensing Image by Integrating DCNN with Short Range Conditional Random Field(SRCRF)

计量

结合DCNN与短距条件随机场的遥感影像道路提取

doi: 10.13203/j.whugis20210464

1. 郑州大学, 地球科学与技术学院, 河南郑州, 450052;

2. 郑州大学, 信息工程学院, 河南郑州, 450052;

3. 中国气象科学研究院郑州大学生态气象联合实验室郑州, 450052

作者简介:
赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

English Abstract

Road Extraction from Remote Sensing Image by Integrating DCNN with Short Range Conditional Random Field(SRCRF)

1. School of Earth Science and Technology, Zhengzhou University, Zhengzhou 450052, China;

2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China;

3. Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450052

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留言板

结合DCNN与短距条件随机场的遥感影像道路提取

doi: 10.13203/j.whugis20210464

作者简介: 赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

Road Extraction from Remote Sensing Image by Integrating DCNN with Short Range Conditional Random Field(SRCRF)

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结合DCNN与短距条件随机场的遥感影像道路提取

doi: 10.13203/j.whugis20210464

1. 郑州大学, 地球科学与技术学院, 河南 郑州, 450052; 2. 郑州大学, 信息工程学院, 河南 郑州, 450052; 3. 中国气象科学研究院郑州大学生态气象联合实验室 郑州, 450052

作者简介: 赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

English Abstract

Road Extraction from Remote Sensing Image by Integrating DCNN with Short Range Conditional Random Field(SRCRF)

1. School of Earth Science and Technology, Zhengzhou University, Zhengzhou 450052, China; 2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China; 3. Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450052

全文HTML

目录

作者简介:
赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

1. 郑州大学, 地球科学与技术学院, 河南郑州, 450052;

2. 郑州大学, 信息工程学院, 河南郑州, 450052;

3. 中国气象科学研究院郑州大学生态气象联合实验室郑州, 450052

作者简介:
赫晓慧，教授，博士生导师，主要从事人工智能，遥感影像处理及数据挖掘。hexh@zzu.edu.cn

1. School of Earth Science and Technology, Zhengzhou University, Zhengzhou 450052, China;

2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China;

3. Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450052