Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images

arXiv:2403.14547v1 Announce Type: cross
Abstract: The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, channel transformations (e.g., solarize, grayscale, brightness adjustments) are integrated into data augmentation pipelines for remote sensing (RS) image classification tasks. However, contradicting beliefs exist about their proper applications to RS images. A common point of critique is that the application of channel augmentation techniques may lead to physically …

abstract application art arxiv augmentation classification cs.cv cs.lg data deep learning image images information pipelines results role semi-supervised sensing state type