Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning

The high-dimensional data space of hyperspectral images (HSIs) often result in ill-conditioned formulations, which finally leads to many of the high-dimensional feature spaces being empty and the useful data existing primarily in a subspace. To avoid these problems, we use distance metric learning f...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 55; no. 5; pp. 2509 - 2524
Main Authors Dong, Yanni, Du, Bo, Zhang, Liangpei, Zhang, Lefei
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Algorithms
Dimensionality reduction
Dimensions
Distance
ensemble learning
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Learning
Learning systems
local discriminative distance metrics
Machine learning
Measurement
Principal component analysis
Reduction
Training
Online AccessGet full text

Cover

More Information
Summary:The high-dimensional data space of hyperspectral images (HSIs) often result in ill-conditioned formulations, which finally leads to many of the high-dimensional feature spaces being empty and the useful data existing primarily in a subspace. To avoid these problems, we use distance metric learning for dimensionality reduction. The goal of distance metric learning is to incorporate abundant discriminative information by reducing the dimensionality of the data. Considering that global metric learning is not appropriate for all training samples, this paper proposes an ensemble discriminative local metric learning (EDLML) algorithm for HSI analysis. The EDLML algorithm learns robust local metrics from both the training samples and the relative neighborhood of them and considers the different local discriminative distance metrics by dealing with the data region by region. It aims to learn a subspace to keep all the samples in the same class are as near as possible, while those from different classes are separated. The learned local metrics are then used to build an ensemble metric. Experiments on a number of different hyperspectral data sets confirm the effectiveness of the proposed EDLML algorithm compared with that of the other dimension reduction methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2645703