Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

Jinliang An,Xiaoxiao Ma,Yuzhen Song,Yuwei Guo,Xiangrong Zhang

doi:10.3390/rs11151822

Abstract

Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation.

Highlights

Hyperspectral images can offer wealth ground objects information which make the precision analysis of different material come true
Dimensionality reduction (DR) is a critical preprocessing of hyperspectral images which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images [1,2]
The labeled samples are needed in supervised methods which may lead to a more discriminative low dimensionality subspace, but the cost of labeling samples in hyperspectral images is extreme high which may limit the application of these methods in practice

Summary

Introduction

Hyperspectral images can offer wealth ground objects information which make the precision analysis of different material come true. According to whether the labeled training samples are used or not, the existing dimensionality reduction methods can be categorized into three categories: unsupervised, supervised and semi-supervised. The labeled samples are needed in supervised methods which may lead to a more discriminative low dimensionality subspace, but the cost of labeling samples in hyperspectral images is extreme high which may limit the application of these methods in practice. The unsupervised methods obtain the low dimensionality representation by mining the structure characters of original dataset and need no label samples and Principal Components Analysis (PCA) is the most famous unsupervised criterion. To jointly consider the advantages of supervised and unsupervised methods, the semi-supervised criterion utilizes the label information from a few labeled samples and the structure information extracted from a large number of unlabeled samples [3,4]

Methods

Results

Discussion

Conclusion