• 遥感科学与应用技术 •

### 基于特征分离机制的深度学习植被自动提取方法

1. 1. 安徽大学资源与环境工程学院,合肥 230601
2. 安徽省地理信息智能技术工程研究中心,合肥 230000
• 收稿日期:2020-10-27 出版日期:2021-09-25 发布日期:2021-11-25
• 通讯作者: *吴艳兰（1973— ）,女,贵州凯里人,博士,教授,硕士生导师,主要研究方向为遥感大数据智能技术。 E-mail: wuyanlan@ahu.edu.com
• 作者简介:周欣昕（1996— ）,女,安徽宿州人,硕士生,研究方向为深度学习遥感影像信息提取。E-mail: zxx0331@163.com
• 基金资助:
国家自然科学基金项目(41971311);安徽省科技重大专项(18030801111);2019年省科技支撑计划项目(K120335009)

### Automatic Vegetation Extraction Method based on Feature Separation Mechanism with Deep Learning

ZHOU Xinxin1(), WU Yanlan1,2,*(), LI Mengya1, ZHENG Zhiteng1

1. 1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. Anhui Geographic Information Intelligent Technology Engineering Research Center, Hefei 230000, China
• Received:2020-10-27 Online:2021-09-25 Published:2021-11-25
• Supported by:
National Natural Science Foundation of China(41971311);Science and Technology Major Special Project of Anhui Province(18030801111);2019 Provincial Science and Technology Support Plan(K120335009)

Abstract:

With the improvement of the spatial resolution of remote sensing images, the high-precision extraction of vegetation information is of great significance for understanding the changing laws of surface vegetation and evaluating ecological regions. Aiming at the problem that the existing vegetation extraction methods are difficult to extract the yellow vegetation information and it is difficult to realize the vegetation cross-season extraction, this paper proposes a deep learning semantic segmentation network of vegetation extraction method based on the feature separation mechanism using the GaoFen-2 satellite data. The network adds a feature separation mechanism that combines separable convolution and atrous spatial pyramid on the basis of Densenet. The atrous spatial pyramid effectively reduces the loss of information while acquiring spatial features of different scales. This network takes the high-level semantic information of vegetation into account in complex background. The feature information is enhanced while the accuracy of the model is improved. In order to reduce the calculation amount and the parameter amount of the atrous spatial pyramid, a separable convolution layer is used to replace its original convolution layer. In this paper, we constructed a high-precision cross-season vegetation sample database. Using the method proposed in this article, vegetation information is extracted from remote sensing images, which solves the problem that it is difficult to effectively extract the yellow vegetation information. This paper selects overall accuracy, F1 score, and intersection over union as evaluation indicators to compare and analyze the accuracy of different traditional methods and deep learning methods. The experimental results show that the method proposed in this paper is better than traditional vegetation extraction methods and other deep learning methods according to the three evaluation indicators. The F1 score reaches 91.91%, the overall accuracy reaches 92.79%, and the intersection ratio reaches 85.10%. The general verification experiment of the different vegetation types in the remote sensing image of GaoFen-2 has been carried out. The experimental results show that the method in this paper can completely extract the vegetation types of woodland, arable land, and grassland in the image. The generalization of vegetation extraction is verified on the remote sensing images of GaoFen-1, GaoFen-6, and SuperView-1.The results show that the method proposed in this paper has a certain general ability. It can realize the automatic and high-precision extraction of vegetation from high resolution remote sensing images. The results of this paper can provide data reference for urban ecological environment evaluation and vegetation application research.