Improving Image Clustering through Sample Ranking and Its Application to Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 14; p. 3317
• Main Authors: Li, Qinglin, Qiu, Guoping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2022
• Subjects: Algorithms; Clustering; Entropy; majority voting; Model accuracy; Neural networks; Performance evaluation; Population density; Ranking; Remote sensing; remote sensing images; sample ranking; Semantics; Visual discrimination learning
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14143317

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.