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.

翻译：数据增强在深度学习（DL）方法中的应用，对于在监督、半监督及自监督图像分类任务中取得最优结果具有重要作用。其中，通道变换（例如曝光过度、灰度化、亮度调整）已被集成至遥感（RS）图像分类任务的数据增强管道中。然而，关于这些变换在遥感图像中的合理应用存在相互矛盾的认知。常见的质疑点在于：通道增强技术的应用可能导致物理上不一致的光谱数据（即像素特征）。为厘清这一争议，我们提出一种评估方法，用于判断通道增强技术是否影响遥感图像的物理信息。该方法通过计算一个评分来度量像素特征在时间序列中的一致性——该序列可能因采集条件或植被物候状态等因素产生自然偏差。通过对比原始像素特征与增强后像素特征的评分，我们评估其物理一致性。基于多标签图像分类任务的实验结果表明，当通道增强产生的评分超过原始像素特征预期偏差范围时，无法提升未使用增强的基线模型的性能。