Iterative Random Training Sample Selection for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in hyperspectral image classifications is the random training sample selection which produces inconsistent results. A general approach to resolving this problem is so-called k-fold method which implements randomly selected training samples k times and takes their average with respect to the standard deviation to be used describe a confidence interval. This paper develops an approach to mitigating such a random issue by introducing an iterative process to remove uncertainty caused by randomness. Its idea is to repeatedly feedback the classification results in an iterative manner that the randomness caused by the randomly selected samples can be largely reduced. The iterative process is terminated as long as the classification results obtained by two consecutive iterations agree with a prescribed tolerance. Experimental results demonstrate that our proposed method works very effectively not only to reduce result inconsistency but also to improve classification results.