CP2M通过分块聚类混合增强，提升遥感图像分割的泛化能力。
CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation
- 将四张图拼接后，用连通域算法聚类补丁，保持空间一致性
- 在ISPRS Potsdam数据集上显著降低过拟合，提升分割精度
- 适合遥感图像少样本场景下的模型训练
遥感图像分割对地球观测至关重要，支撑环境监测与城市规划等应用。由于遥感数据标注有限，大量研究聚焦于数据增强以缓解深度学习模型的过拟合问题。然而，现有增强策略多依赖简单变换，难以充分提升数据多样性与模型泛化能力。本文提出一种新型增强方法——分块聚类混合马赛克（Clustered-Patch-Mixed Mosaic, CP2M），结合马赛克增强与分块聚类混合阶段。前者通过四张随机图像合成新样本，后者利用连通域标记算法确保增强后图像保持空间连贯性，避免引入无关语义。在ISPRS Potsdam数据集上的实验表明，CP2M显著缓解过拟合，在分割准确率与模型鲁棒性方面建立新基准。
原文摘要 · Abstract (English)
Remote sensing image segmentation is pivotal for earth observation, underpinning applications such as environmental monitoring and urban planning. Due to the limited annotation data available in remote sensing images, numerous studies have focused on data augmentation as a means to alleviate overfitting in deep learning networks. However, some existing data augmentation strategies rely on simple transformations that may not sufficiently enhance data diversity or model generalization capabilities. This paper proposes a novel augmentation strategy, Clustered-Patch-Mixed Mosaic (CP2M), designed to address these limitations. CP2M integrates a Mosaic augmentation phase with a clustered patch mix phase. The former stage constructs a new sample from four random samples, while the latter phase uses the connected component labeling algorithm to ensure the augmented data maintains spatial coherence and avoids introducing irrelevant semantics when pasting random patches. Our experiments on the ISPRS Potsdam dataset demonstrate that CP2M substantially mitigates overfitting, setting new benchmarks for segmentation accuracy and model robustness in remote sensing tasks.
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