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| AVIRIS高光谱数据空-谱特征在植被分类中的对比分析 | |
| 作者姓名： | 付元元 杨贵军 段丹丹 张永涛 顾晓鹤 杨小冬 徐新刚 李振海 |
| 作者单位： | 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 国家农业信息化工程技术研究中心，北京 100097 北京农业智能装备技术研究中心，北京 100097 江苏诺丽慧农农业科技有限公司，南京 210001 北京市农业物联网工程技术研究中心，北京 100097 |
| 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。 |
| 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 |
| 收稿时间： | 2019-11-27 |
Comparison analysis of spatial and spectral feature in vegetation classification based on AVIRIS hyperspectral image |
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| Authors: | Fu Yuanyuan Yang Guijun Duan Dandan Zhang Yongtao Gu Xiaohe Yang Xiaodong Xu Xingang Li Zhenhai |
| Abstract: | 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 indicated that: (1) The proposed hybrid feature extraction method PCA_ScatterMatrix got the highest overall classification accuracy on both data sets (82.7% and 86.5%) among three classic subspace feature extraction methods (PCA, ICA and LDA) and respectively improved overall classification accuracy by 1.5% and 2.5% on both data sets, comparing to original PCA; (2) Compared to spectral features, spatial feature extraction methods generally got higher overall classification accuracy, especially Gabor spatial features got the highest overall classification accuracy on both data sets (95.5% and 96.7%). The results suggest that the proposed method is effective in vegetation classification of hyperspectral image and the spatial features play a much more important role in vegetation classification of hyperspectral image, comparing with spectral features. |
| Keywords: | hyperspectral image vegetation classification spectral feature spatial feature hybrid feature extraction method scatter-matrix principal component analysis |
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