"LOW SUPERVISION" DEEP CLUSTER CHANGE DETECTION (CDCLUSTER) ON REMOTE SENSING RGB DATA: TOWARDS THE UNSUPERVISING CLUSTERING FRAMEWORK
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM(2023)
Abstract
This paper is concerned with the change detection issue in remote sensing images. This problem is not trivial since the notion of change depends on the application. Moreover, classical supervised deep learning methods have to deal with the limited amount of labelled data available. Based on existing deep learning techniques that exploit unsupervised clustering to assign labels to entire images, we adapt them to the change detection problem by using siamese backbones and extracting pixel-wise results. As fully unsupervised experiments lead to unstable results, we suggest "low supervision" strategy composed of a warm-up stage with few labeled data able to drive the following unsupervised learning through reliable solutions. Preliminary experiments show reliable change maps.
MoreTranslated text
Key words
Change Detection,Semi-supervised Learning,Unsupervised Learning,Clustering,Multitemporal images,Deep learning
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined