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方法中的应用，对于在有监督、半监督和自监督图像分类任务中取得最先进的结果具有重要作用。特别是，通道变换（如曝光、灰度化、亮度调整）已被集成到遥感图像分类任务的数据增强流程中。然而，关于其在遥感图像上的正确应用方式存在相互矛盾的观点。一个常见的批评点是，通道增强技术的应用可能导致物理上不一致的光谱数据（即像素光谱曲线）。为了阐明这一开放性的争论，我们提出了一种方法来评估通道增强技术是否会影响遥感图像的物理信息。为此，所提出的方法通过计算一个评分来度量像素光谱曲线在时间序列中的对齐程度，该时间序列本身可能因采集条件或植被物候状态等因素而自然存在偏差。我们通过比较原始像素光谱曲线与增强后像素光谱曲线的评分来评估物理一致性。在多标签图像分类任务上的实验结果表明，若通道增强导致的评分超过了原始像素光谱曲线的预期偏差范围，则无法提升未使用增强训练的基线模型的性能。