  Label information plays an important role in supervised hyperspectral image
classification problem. However, current classification methods all ignore an
important and inevitable problem---labels may be corrupted and collecting clean
labels for training samples is difficult, and often impractical. Therefore, how
to learn from the database with noisy labels is a problem of great practical
importance. In this paper, we study the influence of label noise on
hyperspectral image classification, and develop a random label propagation
algorithm (RLPA) to cleanse the label noise. The key idea of RLPA is to exploit
knowledge (e.g., the superpixel based spectral-spatial constraints) from the
observed hyperspectral images and apply it to the process of label propagation.
Specifically, RLPA first constructs a spectral-spatial probability transfer
matrix (SSPTM) that simultaneously considers the spectral similarity and
superpixel based spatial information. It then randomly chooses some training
samples as "clean" samples and sets the rest as unlabeled samples, and
propagates the label information from the "clean" samples to the rest unlabeled
samples with the SSPTM. By repeating the random assignment (of "clean" labeled
samples and unlabeled samples) and propagation, we can obtain multiple labels
for each training sample. Therefore, the final propagated label can be
calculated by a majority vote algorithm. Experimental studies show that RLPA
can reduce the level of noisy label and demonstrates the advantages of our
proposed method over four major classifiers with a significant margin---the
gains in terms of the average OA, AA, Kappa are impressive, e.g., 9.18%, 9.58%,
and 0.1043. The Matlab source code is available at
https://github.com/junjun-jiang/RLPA
