空-谱协同流形重构的高光谱影像分类

鉴于传统高光谱影像分类大都采用监督学习方法,且仅利用了光谱信息,未考虑影像空间特征和流形结构。提出一种基于空-谱协同流形重构误差的高光谱影像分类方法,该算法基于高光谱影像中地物分布的空间一致性,利用少量标记的样本和大量的无标记空间近邻样本来进行半监督学习,并利用测试样本在每一子流形上的重构误差来表征相似性,实现鉴别分类。在Indian Pines和University of Pavia数据集上的实验结果表明,本文方法的分类精度在各种条件下要优于其他分类算法,其最高总体精度分别达到了95.67%和91.92%。该算法将高光谱遥感影像中的空间-光谱信息融入不同地物的子流形结构表征,在训练样本数量较少时仍能得到好的分类效果,有效提升了分类性能。

Hyper-spectral image classification using spatial-spectral manifold reconstruction

In recent years, several supervised learning methods have been introduced in hyperspectral image (HSI) classification. However, these methods use only spectral information without taking into account the spatial features and manifold structures of HSIs. To overcome this problem, a new classification method was proposed for HSI classification, combining spatial-spectral features and manifold reconstruction. Based on the spatial consistency of ground objects distribution in HSIs, the proposed algorithm used a small number of labeled samples and large number of unlabeled spatial neighbor samples to perform semisupervised learning, and utilized the reconstruction error of test samples in each submanifold to represent the similarities for discriminant classification. Experimental results obtained from the Indian Pines and University of Pavia data set reveal that the proposed method exhibits a higher classification accuracy compared to other classification algorithms under various training conditions, the highest overall accuracy achieved in the two cases being 95.67% and 91.92%, respectively. The proposed method integrates spatial-spectral information to represent the submanifold structure of different land objects, exhibits superior discrimination performance, especially for a small number of training samples, and effectively improves the performance of HSI classification.