期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 51, 期 1, 页码 242-256出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2012.2197860
关键词
Classification; feature extraction; hyperspectral image (HSI); remote sensing; tensor
类别
资金
- National Basic Research Program of China (973 Program) [2011CB707105]
- National Natural Science Foundation of China [40930532, 41101336, 41061130553]
- Australia Research Council Discovery Project [ARC DP-120103730]
In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据