  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 inconsistent spectral
data (i.e., pixel signatures). To shed light on the open debate, we propose an
approach to estimate whether a channel augmentation technique affects the
physical information of RS images. To this end, the proposed approach estimates
a score that measures the alignment of a pixel signature within a time series
that can be naturally subject to deviations caused by factors such as
acquisition conditions or phenological states of vegetation. We compare the
scores associated with original and augmented pixel signatures to evaluate the
physical consistency. Experimental results on a multi-label image
classification task show that channel augmentations yielding a score that
exceeds the expected deviation of original pixel signatures can not improve the
performance of a baseline model trained without augmentation.
