由于高光谱图像具有波段多、波段间信息冗余、空间信息相关等特点,提出一种考虑空间相关性的半监督局部保持投影(LPP)的高光谱图像特征提取算法(LPP-SCSSFE)。该算法利用保存高光谱图像空间近邻结构的空间距离,及保持图像光谱相似性的类内判别权值和类间判别权值,定义新的同物异谱、同谱异物像元权值计算函数,结合LPP提取高光谱图像特征,从而最大化同类地物间的相似性和异类地物间的差异性。在Indian Pines和Pavia University两个数据集上,通过高光谱图像分类实验对本文提出的LPP-SCSSFE算法进行验证,算法最高总体分类精度分别达到87.50%和91.29%,优于现有的特征提取算法。结果表明,本文算法充分考虑高光谱图像的空间相关性和光谱相似性,能够有效提取出有代表性的特征,提高分类精度。

Based on the characteristics of multi-band, inter-band information redundancy and spatial information correlation of hyperspectral images, a spatially-correlated and semi-supervised feature extraction (SCSSFE) algorithm with locality preserving projection (LPP) is proposed. This algorithm defines a new pixel weight calculation function for the different spectral characteristics with the same objects and the different objects with the same spectral characteristics to preserve the spatial distance and the spectral similarity of hyperspectral image by means of the neighbor structure in image space and the intra-class and inter-class discriminant weights. Then, the features of hyperspectral images are extracted by the weight function combined with LPP. Thus the similarity among the same objects and the discrepancy among the different objects are maximized. The proposed LPP-SCSSFE algorithm is verified through the hyperspectral image classification experiments on the two datasets of Indian Pines and Pavia University. The highest overall classification accuracies of the LPP-SCSSFE algorithm reach 87.50% and 91.29% for the respective datasets, better than those of the existing feature extraction algorithms. These results indicate that the spatial correlation and the spectral similarity of hyperspectral images are fully taken into account in the proposed algorithm, and thus the more representative features are extracted and the classification accuracy is enhanced.