基于改进分离阈值特征优选的秋季作物遥感分类  

Remote Sensing Classification of Autumn Crops Based on Hybrid FeatureSelection Model Combining with Relief F and Improved Separabilityand Thresholds

作　　者：王庚泽 靳海亮[1] 顾晓鹤[2,3] 杨贵军[3,4] 冯海宽 孙乾[2,3] WANG Gengze;JIN Hailiang;GU Xiaohe;YANG Guijun;FENG Haikuan;SUN Qian(School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China;Key Laboratory of Quantitative Remote Sensing in Agriculture,Ministry of Agriculture and Rural Affairs,Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;Beijing Engineering Research Center for Agriculture Internet of Things,Beijing 100097,China)

机构地区：[1]河南理工大学测绘与国土信息工程学院,焦作454000 [2]北京农业信息技术研究中心农业农村部农业遥感机理与定量遥感重点实验室,北京100097 [3]国家农业信息化工程技术研究中心,北京100097 [4]北京市农业物联网工程技术研究中心,北京100097

出　　处：《农业机械学报》2021年第2期199-210,共12页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(41571323);国家重点研发计划项目(2016YFD0300609)。

摘　　要：为了提高秋季作物分类精度,以多时相的Sentinel 2为数据源,以生育进程相近的秋季作物为分类对象,提出一种基于Relief F算法和信息熵改进分离阈值算法(Modified ISEaTH based entropy,EMISE)的多评价准则融合特征优选算法——改进分离阈值组合式特征优选算法(Modified EMISE based Relief F,ReEMISE),并分析了不同特征对秋季作物分类的重要性。首先,利用Relief F算法对特征进行初选,结合EMISE算法对2种评价准则进行融合,再优化初选特征集,进而利用随机森林(Random forest,RF)方法提取农作物种植面积,并与单评价准则的Relief F算法和EMISE算法的随机森林分类精度进行比较。同时,利用多时相光谱特征、传统指数特征、红边指数特征、纹理特征、不同时相波段差值特征、不同时相波段比值特征及优选特征,通过7组不同的特征组合提取秋季作物种植面积,分析不同特征组合对秋季作物分类精度的影响。结果表明:ReEMISE特征优选的随机森林法在特征变量为9个时精度最高,总体精度和Kappa系数分别为95.3918%和0.9397;综合多特征是提高农作物分类精度的关键,在多时相光谱特征基础上分别加入传统指数特征和红边特征,总体精度分别提高1.5021、1.5715个百分点,Kappa系数分别提高0.0198、0.0207。因此综合多特征的ReEMISE特征优选的随机森林法可以有效提高秋作物分类精度和效率。The multi-temporal Sentinel 2 images were used to classify the autumn crops in Gaocheng,Shijiazhuang to provide an important basis for the local agricultural planting structure adjustment.The influence of comprehensive multi-features and feature optimization on the extraction accuracy of autumn crop planting area were analyzed.In order to reduce the influence of high-dimensional features on the performance of the classifier,a filter hybrid feature selection model(ReEMISE)based on improved separability and thresholds combined with the Relief F algorithm was proposed.Firstly,the Relief F dimensionality reduction algorithm was used to select the features.Secondly,the improved separability and thresholds(EMISE)combined with image entropy was used to further optimize the preliminary feature set,and then the EMISE feature importance value was given a Relief F feature weight.Finally,the random forest model was used to extract the crop planting area from the optimized feature subset,and compared with the random forest classification accuracy of the Relief F dimensionality reduction algorithm and the EMISE dimensionality reduction algorithm.The purpose was to ensure the accuracy of classification,minimize the feature dimensions and improve the classification efficiency.Six different types of feature variables were generated based on the Sentinel 2 data with multi-phase and rich spectral information,including multi-temporal spectral features,traditional index features,red-edge index features,texture features,difference features of different time-phase bands,and ratio features of different time-phase bands.On the basis of multi-temporal spectral features,adding different features,totally six groups of different feature combination experiments were constructed to extract autumn crop planting area and verify the classification accuracy of different feature combination.At the same time,the influence of different features on the extraction accuracy of crop planting area was analyzed from two aspects:the importance of features a

关 键 词：秋季作物 遥感分类 特征优选 改进分离阈值组合式特征优选算法 随机森林 

分 类 号：S127[农业科学—农业基础科学]