中文摘要：

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。

英文摘要：

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