In remote sensing scene images, the complexity of details amplifies intra-class diversity and inter-class similarity. Cross-scene classification enhances the model's discriminative capability by learning cross domain data correlations, thereby effectively mitigating the impacts caused by inter-class similarity and intra-class diversity. Domain generalization (DG) as a pivotal technique in cross-scene classification, has been widely studied by scholars due to its strong applicability. In this paper, a normalization-based style feature elimination method is proposed. The original features and normalized features are decomposed into their respective style and content components. Content features typically play a decisive role in classification tasks, while style features are slightly redundant, so we set the elimination of style features as our goal. However, style removal inevitably incurs content alteration due to the ill-defined boundary between content and style. We address this challenge by frequency-domain analysis based on the Discrete Fourier Transform, where amplitude and phase are explicitly decoupled into style and content features through mathematical analysis. The adaptable network eliminates style features to adjust the style-to-content ratio in images for learning domain-invariant features. Experimental results and systematic analyses validate the effectiveness of the pro-posed method in DG for scene classification tasks.