论文标题
通过样本排名及其应用于遥感图像来改善图像聚类及其应用
Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images
论文作者
论文摘要
图像聚类是一种非常有用的技术，可广泛应用于各个区域，包括遥感。最近，通过自我监督学习的视觉表示大大提高了图像聚类的性能。为了进一步改善训练有素的聚类模型，本文提出了一种新方法，该方法是根据对当前群集的构成的置信度首先在每个集群中排名样本，然后使用排名来制定加权的交叉输入损失以训练该模型。为了对样品进行排名，我们开发了一种根据当前群集的样本可能性的可能性，该方法是根据它们是否位于人口稠密的邻域中的样本的可能性，而在训练模型的同时，我们给出了加权排名样本的策略。我们提出了广泛的实验结果，这些结果表明新技术可用于改善最新的图像聚类模型，从而实现准确性的性能增长范围从$ 2.1 \％\％$到$ 15.9 \％\％$。从遥感中执行各种数据集的方法，我们表明我们的方法可以有效地应用于遥感图像。
Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the State-of-the-Art image clustering models, achieving accuracy performance gains ranging from $2.1\%$ to $15.9\%$. Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote--sensing images.
