基于局部保持线性判别嵌入特征提取的光谱图像分类

LPLDE (Locally Preserving Linear Discriminant Embedding) Method of Feature Extraction for Hyperspectral Image Classification

针对难于获得足够多的高光谱图像训练样本的问题,基于流形学习标准、Fisher标准和最大边缘标准,提出了一种适用于高光谱图像小样本问题的局部保持线性判别嵌入(LPLDE)监督线性流形学习特征提取方法。LPLDE方法利用类内近邻图和类间近邻图描述类内的紧性和类间的可分性,有效地避免了因类内离散度矩阵奇异导致的小样本问题,具有更好的判别性能,更适合于分类问题。高光谱数据的实验结果表明了该方法的有效性。

In view of the difficulty to get enough training vel supervised linear manifold learning feature extraction samples in hyperspectral image, this paper presents a no- method based on the manifold learning, Fisher criterion and Maximum Margin Criterion, named LPLDE by us, for hyperspectral image classification with nearest neighbor （NN） classifier. The intraclass compactness and interclass separability of hyperspeetral data are respectively characterized by within-class neighboring graph and between-class neighboring graphs via embedding. The LPLDE method which efficiently avoids the within-class scatter matrix singularity caused by small-sample-size problem has better discriminative performance and is more suitable for classification. Experimental results on hyperspectral datasets and their analysis demonstrate preliminarily the efficiency of our LPLDE method as compared with other existing methods.