基于局部线性嵌入的高光谱影像特征提取算法

特征提取能够消除冗余信息,提高高光谱数据处理的精度和计算效率,是分类等分析必要的预处理手段.传统特征提取算法基于线性变换,无法准确描述高、低维特征空间的关系,因此采用一种新型非线性特征提取算法,即局部线性嵌入(LLE,Locally Linear Em-bedding),挖掘高光谱影像的本征信息.针对分类问题,使用训练样本类别属性修正距离矩阵,并借鉴LLE计算未知样本低维映射的方法求解测试样本的特征向量,实现监督局部线性嵌入(SLLE,Supervised Locally Linear Embedding).使用机载可见光/红外成像光谱仪数据,与3种分类算法结合进行测试,实验结果表明:SLLE优于线性特征提取算法,能够解决高光谱影像的小样本分类问题.

Feature extraction of hyperspectral image based on locally linear embedding

Feature extraction can eliminate the redundant information hidden in the hyperspectral image. It is a necessary preprocessing step of the hyperspectral image analysis system, the classification for instance, to improve the precision and efficiency. Traditional feature extraction algorithms are based on linear transformation, which could not accurately describe the relationship between the original and reduced feature spaces. Therefore, locally linear embedding (LLE), the representative algorithm of nonlinear feature extraction, was adopted to reveal the intrinsic information of the hyperspectral image. For classification, the class labels of the training samples were utilized to adjust the distance matrix and the feature vectors of the test samples were calculated in the way that LLE mapped the unknown samples, realizing the supervised locally linear embedding (SLLE). In the experiment, the nonlinear feature extraction method was combined with three different classifiers and evaluated using the data collected by airborne visible/infrared imaging spectrometer. The experiments show that SLLE is superior to the linear feature extraction methods and can solve the small training set problem of classifying hyperspectral image.