| 分散矩阵特征选择方法改进及在高光谱影像植被分类中的应用 |
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| 引用本文： | 夏道平,付元元,王纪华,郑晓东.分散矩阵特征选择方法改进及在高光谱影像植被分类中的应用[J].农业工程学报,2016,32(21):196-201. |
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| 作者姓名： | 夏道平 付元元 王纪华 郑晓东 |
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| 作者单位： | 1. 三峡大学科技学院，宜昌 443002; 湖北省建筑质量检测装备工程技术研究中心，宜昌 443002;2. 浙江大学遥感与信息技术应用研究所,杭州,310029;3. 浙江大学遥感与信息技术应用研究所，杭州 310029; 北京农业质量标准与检测技术研究中心，北京 100097; 三峡大学计算机与信息学院，宜昌 443002;4. 三峡大学科技学院,宜昌,443002 |
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| 基金项目： | 湖北省建筑质量检测装备工程技术研究中心基金项目(CQTE201603)；国家自然科学基金(41371349) |
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| 摘 要： | 基于传统分散矩阵的特征选择方法易选出具有一定区分性但相互冗余的特征,这些冗余的特征制约了高光谱影像分类正确率的提高,针对此问题,该文对传统方法进行了改进,首先计算每2个类别的基于分散矩阵的可分性值,然后将它们的平均值作为特征选择准则,最后利用序列浮点向前搜索算法选出特定数量的特征,用于后续分类。将所选特征的均方相关系数作为冗余性度量,定量化衡量了所提出方法克服选择冗余特征的能力。利用一景常用的AVIRIS高光谱植被影像,从分类正确率的角度,比较了所提出方法与几种典型的基于互信息和基于可分性准则的特征选择方法,在高光谱影像植被分类中的性能。试验结果表明改进的特征选择方法能较好的避免选择相互冗余的特征,与基于互信息的特征选择方法相比,基于分散矩阵可分性准则的特征选择方法在总体上能获得较高的分类正确率,特别是所提出的特征选择方法,在2个数据集上均获得了最高的总体分类精度87.2%和90.1%,从而阐明了所提出的方法在高光谱影像植被分类中的有效性。
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| 关 键 词： | 植被 分类 光谱分析 高光谱影像 波段选择 基于分散矩阵的可分性准则 互信息 |
| 收稿时间： | 2016/7/13 0:00:00 |
| 修稿时间： | 2016/9/15 0:00:00 |
Improved scatter-matrix-based feature selection method for vegetation classification of hyperspectral image |
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| Xia Daoping,Fu Yuanyuan,Wang Jihua and Zheng Xiaodong.Improved scatter-matrix-based feature selection method for vegetation classification of hyperspectral image[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(21):196-201. |
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| Authors: | Xia Daoping Fu Yuanyuan Wang Jihua and Zheng Xiaodong |
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| Institution: | 1. College of Science and Technology, China Three Gorges University, Yichang, 443002, China; 2. Hubei Engineering Research Center of Construction Quality Testing Equipment, Yichang, 443002, China,3. Institute of Agricultural Remote Sensing and Information Technique, Zhejiang University, Hangzhou 310029, China,3. Institute of Agricultural Remote Sensing and Information Technique, Zhejiang University, Hangzhou 310029, China; 4. Beijing Research Center for Agricultural Standards and Testing, Beijing 100097, China; 5. College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China and 1. College of Science and Technology, China Three Gorges University, Yichang, 443002, China |
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| Abstract: | Abstract: Due to the advances in hyperspectral sensor technology, hyperspectral images have gained a great attention in the precision agriculture. Compared to multispectral images, e.g., Landsat TM (thematic mapper) and MODIS (moderate-resolution imaging spectroradiometer) images, hyperspectral images have higher spectral resolution and provide more contiguous spectrum. Thus, hyperspectral images are expected to have good capability in quantifying vegetation biophysical and biochemical attributes which can reflect crop growth status and guide site-specific agricultural management. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from others. It is easy to distinguish vegetated areas from other surface types by setting the threshold of normalized difference vegetation index (NDVI). As to the discrimination of different vegetation types using hyperspectral image, it is a typical hyperspectral image classification problem. The scatter-matrix-based class separability measure is often favored and chosen as a selection criterion in feature selection due to its simplicity and robustness. The scatter-matrix-based class separability measure is constructed by using 2 of 3 scatter matrices which are within-class scatter matrix, between-class scatter matrix and total scatter matrix. Traditionally, these scatter matrices are calculated from the perspective of all classes. However, direct optimization of this measure tends to select a set of discriminative but mutually redundant features, which restricts the improvement of classification accuracy. In order to avoid selecting mutually redundant features as much as possible, this study proposes an improved scatter-matrix-based feature selection method, which tries to calculate scatter-matrix-based class separability values for each pair of classes and then takes the average of all the pairwise class separability values as the final selection criterion. Feature selection is performed by maximizing the criterion using sequential floating forward search (SFFS). In order to verify whether the proposed feature selection method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the proposed feature selection method and the conventional scatter-matrix-based feature selection method, and a quantitative comparison was conducted. The classification accuracy of the proposed method was compared with that of several representative feature selection methods that were respectively based on MI (mutual information) and class separability measure. The experiments and comparative analyses were conducted with a widely used hyperspectral image, which was collected over the agriculture 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 feature selection method could better alleviate the problem of selecting mutually redundant features, compared to the feature selection method of conventional scatter-matrix-based class separability measure; (2) Compared with the MI-based feature selection methods, the scatter-matrix-based feature selection methods generally got higher classification accuracies, and especially the proposed feature selection method produced the highest classification accuracies on both data sets (87.2% and 90.1%) for vegetation classification of hyperspectral image. The comparative experiments on the classification of a typical agricultural hyperspectral image demonstrate the effectiveness of the proposed feature selection method in the vegetation classification of hyperspectral image. |
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| Keywords: | vegetation classification spectrum analysis hyperspectral image band selection scatter-matrix-based class separability measure mutual information |
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