关键词: 特征提取, 降维, 监督局部线性嵌入, 流形学习, 高光谱图像分类

Abstract: Hyperspectral image has high spectral dimension, vast data and altitudinal interband redundancy, which brings problems to image classification. To effectively reduce dimensionality and improve classification precision, a new extraction method of nonlinear manifold learning feature based on Supervised Local Linear Embedding (SLLE) for classification of hyperspectral image was proposed in this paper. A data point's k Nearest Neighbours (NN) were found by using new distance function which was proposed according to prior class-label information. Because the intra-class distance is smaller than inter-class distance, classification is easy for SLLE algorithm. The experimental results on hyperspectral datasets and UCI data set demonstrate the effectiveness of the presented method.

Key words: feature extraction, dimensionality reduction, Supervised Locally Linear Embedding (SLLE), manifold learning, hyperspectral image classification