刘舒,姜琦刚,马玥,肖艳,李远华,崔璨.基于多目标遗传随机森林特征选择的面向对象湿地分类[J].农业机械学报,2017,48(1):119-127.
LIU Shu,JIANG Qigang,MA Yue,XIAO Yan,LI Yuanhua,CUI Can.Object-oriented Wetland Classification Based on Hybrid Feature Selection Method Combining with Relief F, Multi-objective Genetic Algorithm and Random Forest[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(1):119-127.
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基于多目标遗传随机森林特征选择的面向对象湿地分类   [下载全文]
Object-oriented Wetland Classification Based on Hybrid Feature Selection Method Combining with Relief F, Multi-objective Genetic Algorithm and Random Forest   [Download Pdf][in English]
投稿时间:2016-09-02  
DOI:10.6041/j.issn.1000-1298.2017.01.016
中文关键词:  湿地分类  多光谱遥感影像  面向对象  多目标遗传随机森林算法  特征选择
基金项目:东北地区国土资源遥感综合调查项目(85015B01009)
作者单位
刘舒 吉林大学 
姜琦刚 吉林大学 
马玥 吉林大学 
肖艳 吉林大学 
李远华 吉林大学 
崔璨 大连海事大学 
中文摘要:以多时相Landsat8影像和SRTM DEM为数据源,对南瓮河流域进行了面向对象湿地分类。为削弱高维特征集对分类精度的影响,提出一种多目标遗传随机森林组合式特征选择算法(MOGARF)进行特征集优化。利用Relief F算法对完整特征集进行特征初选,再以基于随机森林的封装式多目标遗传算法进一步提取优化特征集。将所得特征集结合随机森林分类法提取湿地信息。并将结果分别与基于完整特征集和仅采用Relief F算法及Boruta算法提取的优化特征集的3种随机森林分类结果对比。试验结果表明,采用MOGARF算法特征选择后,特征维度降低至原来的10%,且分类精度最高,总体精度为92.61%,比其他分类方案提高0.35%~1.94%,Kappa系数为0.9075,袋外误差为7.77%,比其他分类方案降低0.91%~1.48%。利用MOGARF特征选择的随机森林分类法是湿地分类的有效方法。
LIU Shu  JIANG Qigang  MA Yue  XIAO Yan  LI Yuanhua  CUI Can
Jilin University,Jilin University,Jilin University,Jilin University,Jilin University and Dalian Maritime University
Key Words:wetland classification  multi-spectral remote sensed imagery  object-oriented  multi-objective genetic and random forest algorithm  feature selection
Abstract:Recently, researchers adopted object-oriented method to extract wetland distributions. Multi-temporal and multi-sources of data can facilitate the extraction process but meanwhile it enlarges the amount of features. It needs a large quantity of experiment based on the expert knowledge to determine the optimal feature sets and the threshold values. In order to improve the classification accuracy and relief the researchers from large amount of work, a filter-wrapper hybrid feature selection method combining relief F, multi-objective genetic algorithm and random forest was proposed, which was a two-step method. In the first step, relief F algorithm was adopted to select features with class separability. In the second step, multi-objective genetic algorithm based on random forest (MOGARF) was built. Four measures such as out-of-bag (OOB) error of random forest algorithm, dimension of the feature space, correlations among features and the variable weight of relief F algorithm were acted as four objectives of MOGA. The probability whether the feature was expressed was determined by the variable importance measures from random forest algorithm. The crowded distance of each feature collection was calculated and the feature collection with the least crowded distance was the optimal feature set. Nanweng river basin was taken as the study site. Object-oriented classification using random forest classifier was conducted based on the optimal feature set. Then the result was compared with three other random forest classification schemes by using the entire feature set or the feature set selected by relief F algorithm or the Boruta algorithm. The classification scheme with MOGARF had the best performance and the feature dimension was reduced to 10% of the entire one. The overall accuracy reached 92.61% which was 0.35%~1.94% higher than those of the other three schemes with Kappa coefficient of 0.9306. The OOB error of MOGARF was 7.77% which was 0.91%~1.48% lower than those of the other schemes. All these indicated that the MOGARF feature selection method was an effective feature selection method when it was combined with random forest classifier.

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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