Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification

arXiv:2305.12106v2 Announce Type: replace
Abstract: Convolutional neural networks (ConvNets) have been successfully applied to satellite image scene classification. Human-labeled training datasets are essential for ConvNets to perform accurate classification. Errors in human-annotated training datasets are unavoidable due to the complexity of satellite images. However, the distribution of real-world human-annotated label noises on remote sensing images and their impact on ConvNets have not been investigated. To fill this research gap, this study, for the first time, collected real-world labels from 32 …

abstract arxiv classification complexity convolutional convolutional neural networks cs.ai cs.cv datasets distribution errors however human image images impact networks neural networks noise satellite satellite images sensing training training datasets type