Kernel Entropy Component Analysis-Based Robust Hyperspectral Image Supervised Classification

Abstract

Recently, the “noisy label" problem has become a hot topic in supervised classification of hyperspectral images (HSI). Nonetheless, how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing. This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI, which consists of the following steps. First, the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability. Then, the eigenvectors and eigenvalues of the kernel matrix can be obtained by employing symmetric matrix decomposition. Next, the entropy corresponding to each training sample in each class is calculated based on entropy component analysis using the eigenvalues arranged in descending order and the corresponding eigenvectors. Finally, the sigmoid function is applied to the entropy of each sample to obtain the probability distribution. Meanwhile, a decision probability threshold is introduced into the above probability distribution to cleanse the noisy labels of training samples with mislabeled samples for each class. The effectiveness of the proposed method is evaluated by support vector machines on several real hyperspectral data sets. The experimental results show that the proposed KECA method is more efficient than other noisy label detection methods in terms of improving performance of the supervised classification of HSI.