AVIRIS高光谱数据空-谱特征在植被分类中的对比分析  

Comparison analysis of spatial and spectral feature in vegetation classification based on AVIRIS hyperspectral image

作　　者：付元元 杨贵军[1] 段丹丹[3] 张永涛 顾晓鹤 杨小冬[2,5] 徐新刚[2] 李振海 Yuanyuan Fu;Guijun Yang;Dandan Duan;Yongtao Zhang;Xiaohe Gu;Xiaodong Yang;Xingang Xu;Zhenhai Li(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry qfAgriculture 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 Research Center of Intelligent Equipment for Agriculture,Beijing 10097,China;Jiangsu Nuoli Huinong Agricultural Technology Co.,Ltd,Nanjing 210001,China;Beijing Engineering Research Center for Agriculture Internet of Things,Beijing 100097,China)

机构地区：[1]农业农村部农业遥感机理与定量遥感重点实验室,北京农业信息技术研究中心,北京100097 [2]国家农业信息化工程技术研究中心,北京100097 [3]北京农业智能装备技术研究中心,北京100097 [4]江苏诺丽慧农农业科技有限公司,南京210001 [5]北京市农业物联网工程技术研究中心,北京100097

出　　处：《智慧农业（中英文）》2020年第1期68-76,共9页Smart Agriculture

基　　金：国家自然科学青年基金(41801225);国家重点研发计划(2017YFE0122500);北京市自然科学基金(6182011)。

摘　　要：植被分类是高光谱影像分类中的特定应用问题,光谱特征和空间特征是植被分类中常用的两类特征,比较这两类特征的性能,对实际植被分类应用中选择合适的特征类型或两者的有效结合具有指导意义。用主成分分析(PCA)提取光谱特征时,常选择前几个主成分(PCs)作为光谱特征,虽然它们包含较大的信息量但并不能保证较高的类别可分性和分类正确率,针对这一问题本研究提出了一种混合特征提取方法,对高光谱影像在PCA的基础上用改进的基于分散矩阵的特征选择方法选出具有较高类别可分性的PCs用于后续分类。利用一景AVIRIS高光谱植被影像,从分类精度的角度,首先比较了所提出的混合特征提取方法和原始PCA、独立主成分分析(ICA)及线性判别分析(LDA) 3种常用子空间特征提取方法在高光谱影像植被分类中的性能。试验结果表明所提出的混合特征提取方法在研究中数据集1和2上均获得了最高的总体分类正确率,分别为82.7%和86.5%。与原始PCA相比,本研究提出的混合特征提取方法的总体分类正确率,在数据集1和2上分别提高了1.5%和2.5%。由此阐明了所提出的混合特征提取方法在高光谱植被分类中的有效性。对光谱特征和空间特征在高光谱影像植被分类性能的比较中,总体上空间特征获得的分类正确率比光谱特征高,特别是Gabor特征,在两个数据集上均获得了最高的总体分类正确率分别为95.5%和96.7%。由此表明空间特征较光谱特征在高光谱影像植被分类中更具优势。本研究结果为后续改进空-谱特征方法及其两者有效结合,进一步提高植被分类正确率提供了参考。With the development of hyperspectral sensor technology and remote sensing data acquisition platform,the application of hyperspectral data is becoming more and more popular in precision agriculture.Spectral features and spatial features are two main kinds of features used in hyperspectral image classification.The comparison of spectral features and spatial features in vegetation classification of hyperspectral image is a special application in hyperspectral image classification.Therefore,this study compared the performance of several typical spectral features and spatial features in vegetation classification of hyperspectral image.The considered spatial features include grey level co-occurrence matrix(GLCM)based features,Gabor features and morphological features.The considered spectral feature selection or extraction methods include minimal-redundancy-maximal-relevance(mRMR),joint mutual information(JMI),conditional mutual information maximization(CMIM),double input symmetrical relevance(DISR),Jeffreys-Matusita(JM),principal component analysis(PCA),independent component analysis(ICA)and linear discriminant analysis(LDA).PCA,an effective subspace feature extraction method,is widely used in the feature extraction of hyperspectral image.The first several principal components(PCs)are usually selected as spectral features in hyperspectral image classification.However,the first several PCs have no guarantee to achieve good class separability and classification accuracy.Considering that,a hybrid feature extraction approach named as PCA_ScatterMatrix was proposed which combined PCA and an improved scatter-matrix-based feature selection method,aiming to select PCs with high class separability and get high overall classification accuracy.The experiments and comparative analyses were conducted with a widely used hyperspectral image,which was collected over the agricultural area in northwestern Indiana,USA(United States of America)by the AVIRIS(Airborne Visible/Infrared Imaging Spectrometer).The experimental results indicate

关 键 词：高光谱影像 植被分类 光谱特征 空间特征 混合特征提取方法 分散矩阵 主成分分析 

分 类 号：S127[农业科学—农业基础科学]