Training redefinition with entropy-based structure set density for supervised hyperspectral imagery classification

Abstract

ABSTRACT Reliable labelled samples have always played a vital role in the supervised paradigm of hyperspectral imagery (HSI) classification due to the fact that the inclusion of incorrect label information in the training set can seriously degrade the performance of classification methods. Recently, although some inter-class difference-based detection algorithms have been developed to remove mislabelled samples (i.e. noisy labels) in training set, the benefit of contextual information for each sample has not been fully explored yet. In this paper, a training redefinition with entropy-based structure set density (ESSD) method is designed, which consists of following main steps. First, the proposed ESSD method employs an over-segmentation technique to cluster the HSI into many shape-adaptive regions that correspond to sample sets. Then, each sample set is represented with an affine hull (AH) model, which exploits both the similarity and variance of samples within each sample set to adaptively characterize the set. Specifically, considering spectral and spatial weak assumptions among samples in each sample set, the idea of entropy trick-based -nearest neighbour is introduced into each sample set to redefine its structure by removing different class from the sample set. Next, the distance among AH corresponding to each training sample is calculated based on the AH model. Meanwhile, the set-to-set distance is fed to the density peak algorithm to obtain the density of training samples. Finally, a decision-making value is applied to the density of each training sample to cleanse mislabelled samples within noisy training set. Experimental results on real HSI date sets demonstrate the superiority of the proposed method over several well-known training redefinition methods in terms of detection accuracy.