Next Article in Journal
Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models
Next Article in Special Issue
Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery
Previous Article in Journal
3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System
Previous Article in Special Issue
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 506; doi:10.3390/rs9050506

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Xiaofeng Li and Prasad S. Thenkabail
Received: 18 March 2017 / Revised: 10 May 2017 / Accepted: 14 May 2017 / Published: 22 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [2678 KB, uploaded 22 May 2017]   |  

Abstract

The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial–spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method. View Full-Text
Keywords: feature extraction; hypergraph learning; morphological profiles; hyperedge weight estimation feature extraction; hypergraph learning; morphological profiles; hyperedge weight estimation
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Sun, Y.; Wang, S.; Liu, Q.; Hang, R.; Liu, G. Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images. Remote Sens. 2017, 9, 506.

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