通过地理排序正则化，提升多光谱遥感图像自监督学习效果
Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
- 基于球面距离优化，将地理关系嵌入特征空间
- 在多种对比学习算法上超越或持平现有方法
- 适合遥感图像分析与自监督学习研究者参考
自监督学习（SSL）已成为从大规模无标签数据中学习的强大范式，尤其在计算机视觉领域。然而，将SSL应用于多光谱遥感（RS）图像时，因数据具有显著的地理与时间变异性，面临独特挑战与机遇。本文提出GeoRank，一种新型对比自监督学习正则化方法，通过直接优化球面距离，将地理关系嵌入到学习的特征空间中。GeoRank在多种对比学习算法（如BYOL、DINO）上表现优于或匹配现有整合地理元数据的方法，且具备一致性提升能力。此外，本文系统研究了对比学习在多光谱遥感图像中的关键适配问题，包括数据增强的有效性、数据集规模与图像尺寸对性能的影响，以及时间视图的任务依赖性。代码已开源。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data. In this paper, we introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques by directly optimizing spherical distances to embed geographical relationships into the learned feature space. GeoRank outperforms or matches prior methods that integrate geographical metadata and consistently improves diverse contrastive SSL algorithms (e.g., BYOL, DINO). Beyond this, we present a systematic investigation of key adaptations of contrastive SSL for multispectral RS images, including the effectiveness of data augmentations, the impact of dataset cardinality and image size on performance, and the task dependency of temporal views. Code is available at https://github.com/tomburgert/georank.
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