Joint negative–positive-learning based sample reweighting for hyperspectral image classification with label noise

Abstract

Deep neural networks (DNNs) have been widely used for hyperspectral image (HSI) classification. However, the superior performance of DNNs requires accurately labeled samples. In various applications, label noise in large-scale HSI data is often unavoidable and adversely affects the performance of a classifier. Therefore, establishing a robust classification model from an existing DNN classifier for the HSI dataset with label noise is a significant and challenging problem. In this report, we propose a meta-learning reweighting framework based on a joint positive and negative learning (JPNL) method which can adaptively reweight labeled samples to improve the robustness of the classification model. Experimental results on two commonly used hyperspectral imaging (HSI) datasets contaminated with label noise demonstrate significant improvements in classification performance and robustness achieved by the proposed framework, as compared to existing classifiers.