Next Article in Journal
Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model
Next Article in Special Issue
Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification
Previous Article in Journal
Pointing Accuracy of an Operational Polarimetric Weather Radar
Previous Article in Special Issue
Spectral-Spatial Attention Networks for Hyperspectral Image Classification
Article Menu
Issue 9 (May-1) cover image

Export Article

Open AccessArticle

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

1
School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China
2
School of Tourism and Territorial Resources, Jiujiang University, Jiujiang 332005, China
3
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Haiou Yang is the co-first author.
Remote Sens. 2019, 11(9), 1116; https://doi.org/10.3390/rs11091116
Received: 20 April 2019 / Revised: 4 May 2019 / Accepted: 6 May 2019 / Published: 10 May 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
  |  
PDF [4033 KB, uploaded 10 May 2019]
  |  

Abstract

In a real hyperspectral image classification task, label noise inevitably exists in training samples. To deal with label noise, current methods assume that noise obeys the Gaussian distribution, which is not the real case in practice, because in most cases, we are more likely to misclassify training samples at the boundaries between different classes. In this paper, we propose a spectral–spatial sparse graph-based adaptive label propagation (SALP) algorithm to address a more practical case, where the label information is contaminated by random noise and boundary noise. Specifically, the SALP mainly includes two steps: First, a spectral–spatial sparse graph is constructed to depict the contextual correlations between pixels within the same superpixel homogeneous region, which are generated by superpixel image segmentation, and then a transfer matrix is produced to describe the transition probability between pixels. Second, after randomly splitting training pixels into “clean” and “polluted,” we iteratively propagate the label information from “clean” to “polluted” based on the transfer matrix, and the relabeling strategy for each pixel is adaptively adjusted along with its spatial position in the corresponding homogeneous region. Experimental results on two standard hyperspectral image datasets show that the proposed SALP over four major classifiers can significantly decrease the influence of noisy labels, and our method achieves better performance compared with the baselines. View Full-Text
Keywords: hyperspectral image classification; label noise cleansing; spectral–spatial sparse graph; adaptive label propagation hyperspectral image classification; label noise cleansing; spectral–spatial sparse graph; adaptive label propagation
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

Leng, Q.; Yang, H.; Jiang, J. Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification. Remote Sens. 2019, 11, 1116.

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