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Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China
School of Tourism and Territorial Resources, Jiujiang University, Jiujiang 332005, China
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;
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]


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

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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).

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

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