A Superpixel Guided Sample Selection Neural Network for Handling Noisy Labels in Hyperspectral Image Classification

Abstract

Supervised hyperspectral image (HSI) classification has been widely studied and used in many different applications. However, the performance of the supervised classifiers, including the traditional machine learning methods and the deep neural networks, is significantly affected by the inaccurate labeling of training samples, which is a common problem in HSI supervised classification. In this article, we propose a superpixel guided sample selection neural network (S3Net) framework with end-to-end training for handling noisy labels in HSI classification. It includes two stages: sample selection and sample correction. In sample selection, a sample with a small training loss has a higher probability of being the correct label and hence selected from the noisy labels for model training. In order to avoid the error propagation caused by the noisy labels, we utilize a cross-selection update strategy that exchanges selected samples between two neural networks during conventional loss backpropagation. Sample selection is a pruning process, which may cause insufficient training sample problem in HSI classification. To solve this problem, we propose the sample correction strategy to correct the noisy labels by propagating clean label information in the homogeneous regions obtained by superpixel. Experimental results on three public HSI data sets demonstrate the effectiveness of the proposed S3Net framework when handling noisy labels.