Next Article in Journal
Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression
Previous Article in Journal
A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(8), 782; https://doi.org/10.3390/rs9080782

Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images

1
College of Earth Sciences, Jilin University, Changchun 130061, China
2
Lab of Moon and Deepspace Exploration, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3
Department of Information Engineering and Computer Science, University of Trento, 38050 Trento, Italy
4
College of Computer Science and Technology, Jilin University, Changchun 130012, China
*
Authors to whom correspondence should be addressed.
Received: 26 May 2017 / Revised: 25 July 2017 / Accepted: 27 July 2017 / Published: 30 July 2017
View Full-Text   |   Download PDF [4265 KB, uploaded 30 July 2017]   |  

Abstract

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy. View Full-Text
Keywords: hyperspectral imagery; chunklet and discriminative information; discriminative feature metric; affinity propagation; band selection hyperspectral imagery; chunklet and discriminative information; discriminative feature metric; affinity propagation; band selection
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Yang, C.; Tan, Y.; Bruzzone, L.; Lu, L.; Guan, R. Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images. Remote Sens. 2017, 9, 782.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top