Multi-view Collaborative Representation Learning from Noisy Labels for VHR Imagery Classification

摘要

Remote sensing image classification with noisy labels is receiving increasing attention. However, the existing methods ignore the context information of the training sample and judge whether the label is a noise label only by monitoring the loss value of a single sample, which may lead to misjudgment of the sample label. Additionally, these algorithms do not consider constructing pairs of confidence instances to obtain robust potential representations after identifying confidence instances. In this paper, a Multi-view Collaborative Representation Learning (MCRL) approach from noisy labels is proposed to improve the classification performance of very high resolution (VHR) remote sensing images. Specifically, we design a correction strategy based on spatial consistency and confidence-aware mechanisms. This strategy quantitatively measures label reliability by mining the contextual information of labelled samples within the adaptive region. Leveraging the spatial consistency principle and the confidence-aware mechanism to correct and smooth the noisy labels progressively. Moreover, we construct confidence sample pairs by establishing relationships between samples within and between views to obtain robust latent representations, which improves the model's tolerance to noisy labels. Experiments show that the MCRL can significantly reduce the impact of noisy labels on the model and is more competitive than homologous algorithms.