文献类型： 外文期刊

作者： Sun, Yingwei ¹ ; Luo, Jiancheng ¹ ; Xia, Liegang ³ ; Wu, Tianjun ⁴ ; Gao, Lijing ¹ ; Dong, Wen ² ; Hu, Xiaodong ² ; Hai, ¹ ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

2.Univ Chinese Acad Sci, Beijing, Peoples R China

3.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Zhejiang, Peoples R China

4.Changan Univ, Coll Sci, Dept Math & Informat Sci, Xian, Shaanxi, Peoples R China

5.Ningxia Acad Agr & Forestry Sci, Inst Agr Econ & Informat Technol, Ningxia, Peoples R China

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.151； 五年影响因子：3.266 ）

ISSN： 0143-1161

年卷期： 2020 年 41 卷 4 期

页码：

收录情况： SCI

摘要： The basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information.