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.
翻译:图像集群是一种非常有用的技术,广泛应用于各个领域,包括遥感。最近,自我监督的学习的视觉表现极大地改善了图像集群的性能。为了进一步改进经过良好训练的集群模型,本文建议了一种创新方法,即根据对属于当前集群的信任,在每一集群中先定位样本,然后利用排名来制定加权跨热带损失来培训模型。为了对样本进行排序,我们开发了一种方法,根据是否位于人口稠密的居民区来计算属于当前集群的样本的可能性,而为了培训模型,我们给出了对排名样本进行加权的战略。我们提出了广泛的实验结果,表明新技术可用于改进最先进的图像集群模型,实现精确性效绩收益,从2.1美元到15.9美元不等。我们运用了遥感的各种数据集的方法,我们证明我们的方法可以有效地应用于遥感图像。