| 深度学习支持下的丘陵山区耕地高分辨率遥感信息分区分层提取方法 |
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| 引用本文： | 刘巍,吴志峰,骆剑承,孙营伟,吴田军,周楠,胡晓东,王玲玉,周忠发.深度学习支持下的丘陵山区耕地高分辨率遥感信息分区分层提取方法[J].测绘学报,2021,50(1):105-116. |
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| 作者姓名： | 刘巍 吴志峰 骆剑承 孙营伟 吴田军 周楠 胡晓东 王玲玉 周忠发 |
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| 作者单位： | 中国科学院空天信息创新研究院遥感科学国家重点实验室,北京100101;中国科学院大学,北京100049;广州大学地理科学学院,广东 广州 510006;长安大学地质工程与测绘学院,陕西 西安710064;中国科学院空天信息创新研究院遥感科学国家重点实验室,北京100101;贵州师范大学喀斯特研究院,贵州贵阳550001 |
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| 基金项目： | 国家自然科学基金（41631179；41601437）；国家重点研发计划（2017YFB0503600） |
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| 摘 要： | 耕地是丘陵山区稀缺的土地资源,具有地形条件复杂、种植结构多样的特点,导致了山地耕地信息难以快速、准确获取,并且基于传统的遥感数据及遥感监测方法开展山区耕地信息快速自动提取比较困难。针对这一问题,本文以西南山区贵州省息烽县作为试验区,根据地理空间异质性特征,提出分区控制、分层提取的耕地形态信息提取思路,构建了一种地貌单元约束条件下的分区分层耕地形态信息的提取方法。该方法首先根据地貌-植被特征将试验区划分为平坝区、山坡区、林草区3类地理分区;然后在每类分区基础上,根据耕地所呈现的视觉特征划分为不同的类型,对不同类型的耕地分别设计不同的深度学习模型进行分层提取。试验结果证明,该方法对山区复杂地形背景噪声具有较好的抑制作用,所提取的耕地地块信息相比于传统方法更符合实际耕地的实际分布形态,有效地减少了漏提率和错提率。
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| 关 键 词： | 耕地信息 高空间分辨率遥感 分区分层 深度学习 耕地图斑 |
| 收稿时间： | 2019-11-06 |
| 修稿时间： | 2020-04-09 |
A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning |
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| LIU Wei,WU Zhifeng,LUO Jiancheng,SUN Yingwei,WU Tianjun,ZHOU Nan,HU Xiaodong,WANG Lingyu,ZHOU Zhongfa.A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning[J].Acta Geodaetica et Cartographica Sinica,2021,50(1):105-116. |
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| Authors: | LIU Wei WU Zhifeng LUO Jiancheng SUN Yingwei WU Tianjun ZHOU Nan HU Xiaodong WANG Lingyu ZHOU Zhongfa |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;4. School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710064, China;5. School of Karst science, Guizhou Normal University, Guiyang 550001, China |
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| Abstract: | Cropland is a scarce land resource in hilly and mountainous areas, which has the characteristics of complex topographic conditions and diverse planting structures, leading to the difficulty of rapid and accurate acquisition of cropland information in mountainous areas. Therefore, it is difficult to extract the cropland information in mountainous areas quickly and automatically based on the traditional remote sensing data and remote sensing monitoring methods. Aiming at this problem, this paper takes Xifeng County of Guizhou Province in the southwest mountainous area as the experimental area. According to the heterogeneity of geospatial space, this paper proposes the idea of cropland morphological information extraction by geographical division control and stratification extraction, and constructs a method for extracting cropland morphological information based on geographical division control and stratification extraction under the constraints of geomorphic unit. Firstly, according to the geomorphology-vegetation characteristics, the experimental area is divided into three geographical zones: flatland, hillside area and forest. Then, on the basis of each type of partition, the cropland is divided into different types according to the visual characteristics presented by the cropland, and different deep learning models are designed for hierarchical extraction of different types of cropland. The experimental results show that this method has a good suppression effect on the background noise of complex terrain in mountainous areas, and the extracted cropland plot information is more consistent with the actual distribution pattern of the actual cropland compared with the traditional method, which effectively reduces the rate of missing extraction and wrong extraction. |
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| Keywords: | cropland information high-spatial-resolution remote sensing division and stratification deep learning cropland-parcel |
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