Intelligent agent for hyperspectral image classification with noisy labels: a deep reinforcement learning framework

Abstract

ABSTRACT Label accuracy plays an important role in supervised hyperspectral image classification problems. However, labelling can be prone to various types of noise, mainly including human errors and incorrect pseudo-label predictions. Learning from datasets with noisy labels is a critical challenge in practice. To address this issue, we first propose a deep reinforcement learning method named HyperRefine to tackle the label noise problem for hyperspectral image classification. The method utilizes an agent to learn the accuracy of each sample label and determine its suitability to be used in training the classifier model. The agent is trained using a reinforcement signal based on the reward obtained from a small validation set that reflects performance on the target task, where the validation set is generated from the training set by a momentum-based recruiter. Compared to complex hand-crafted methods of label noise cleaning, HyperRefine is easy to implement, yet highly effective. The robustness of the model to different types of label noises is significantly improved by further combining a novel label refinement approach. Experiments on artificially added label noise and pseudo-label noise hyperspectral datasets show that the proposed method can achieve promising performance.