马玥,姜琦刚,孟治国,李远华,王栋,刘骅欣.基于随机森林算法的农耕区土地利用分类研究[J].农业机械学报,2016,47(1):297-303.
Ma Yue,Jiang Qigang,Meng Zhiguo,Li Yuanhua,Wang Dong,Liu Huaxin.Classification of Land Use in Farming Area Based on Random Forest Algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):297-303.
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基于随机森林算法的农耕区土地利用分类研究   [下载全文]
Classification of Land Use in Farming Area Based on Random Forest Algorithm   [Download Pdf][in English]
投稿时间:2015-06-16  
DOI:10.6041/j.issn.1000-1298.2016.01.040
中文关键词:  土地利用分类; 农耕区  随机森林算法  多源信息  特征选择
基金项目:国家自然科学基金项目(41371332)、中国地质调查局项目(1212011220105)和澳门科技发展基金项目(110/2014/A3)
作者单位
马玥 吉林大学 
姜琦刚 吉林大学 
孟治国 吉林大学
中国科学院行星科学重点实验室 
李远华 吉林大学 
王栋 西南林业大学 
刘骅欣 吉林大学 
中文摘要:Land use classification plays an important role in adjusting land structure and developing land resources reasonably, especially in the farming area. The objective of this research is to choose an appropriate method to classify land use type in the farming area. A new classification method, random forest (RF) classifier, was applied to make land use mapping in agricultural cultivation region with multi source information, including multi seasonal spectrum, texture and topographic information. The best classification scheme was chosen to extract land use information, and RF algorithm was used to reduce the dimension of characteristics variables. The RF algorithm, support vector machine, and maximum likelihood classification were used to map agricultural land use, and the applicability of these three different classification methods was analyzed. The result shows that RF classification of land use classification with multi source information effects best, the overall accuracy and Kappa coefficient are 85.54% and 0.8359 respectively. Feature selection method from RF algorithm can effectively reduce the data dimension and ensure the accuracy of classification at the same time. Compared with these three classification methods, RF algorithm performs the highest overall accuracy of 81.08%, which is respectively 9.46% and 5.27% higher than support vector machine and maximum likelihood classification. It is an effective scheme that makes land use classification in the farming area using RF classifier with multi-source information. It provides a fast and feasible method for the division of land use types.
Ma Yue  Jiang Qigang  Meng Zhiguo  Li Yuanhua  Wang Dong  Liu Huaxin
Jilin University,Jilin University,Jilin University;Key Laboratory of Planetary Sciences, Chinese Academy of Sciences,Jilin University,Southwest Forestry University and Jilin University
Key Words:land use classification  farming area  random forest algorithm  multi source information  feature selection
Abstract:

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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