无需标签即可对遥感图像进行高效聚类，适用于不同分布的新数据。
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning
- 用预训练模型提取特征，再通过流形投影降维
- 基于贝叶斯非参数方法自动确定聚类数与归属
- 在多个遥感数据集上优于现有零样本分类方法
本文提出一种针对无标签遥感图像的全图无监督聚类方法。该方法包含三个步骤：(1) 在有标签的源遥感影像数据集上微调预训练深度神经网络（DINOv2），并用其提取目标数据集每张图像的特征向量；(2) 通过流形投影将这些深层特征映射到低维欧氏空间以降低维度；(3) 使用贝叶斯非参数技术对嵌入特征进行聚类，同时推断聚类数量和成员关系。该方法利用异构迁移学习，在特征与标签分布不同的未见数据上仍表现良好。实验表明，该方法在多个遥感场景分类数据集上的性能优于当前最先进的零样本分类方法。
原文摘要 · Abstract (English)
This paper proposes a method for unsupervised whole-image clustering of a target dataset of remote sensing scenes with no labels. The method consists of three main steps: (1) finetuning a pretrained deep neural network (DINOv2) on a labelled source remote sensing imagery dataset and using it to extract a feature vector from each image in the target dataset, (2) reducing the dimension of these deep features via manifold projection into a low-dimensional Euclidean space, and (3) clustering the embedded features using a Bayesian nonparametric technique to infer the number and membership of clusters simultaneously. The method takes advantage of heterogeneous transfer learning to cluster unseen data with different feature and label distributions. We demonstrate the performance of this approach outperforming state-of-the-art zero-shot classification methods on several remote sensing scene classification datasets.
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