DRLnet: Deep Difference Representation Learning Network and An Unsupervised Optimization Framework

DRLnet: Deep Difference Representation Learning Network and An Unsupervised Optimization Framework

Puzhao Zhang, Maoguo Gong, Hui Zhang, Jia Liu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3413-3419. https://doi.org/10.24963/ijcai.2017/477

Change detection and analysis (CDA) is an important research topic in the joint interpretation of spatial-temporal remote sensing images. The core of CDA is to effectively represent the difference and measure the difference degree between bi-temporal images. In this paper, we propose a novel difference representation learning network (DRLnet) and an effective optimization framework without any supervision. Difference measurement, difference representation learning and unsupervised clustering are combined as a single model, i.e., DRLnet, which is driven to learn clustering-friendly and discriminative difference representations (DRs) for different types of changes. Further, DRLnet is extended into a recurrent learning framework to update and reuse limited training samples and prevent the semantic gaps caused by the saltation in the number of change types from over-clustering stage to the desired one. Experimental results identify the effectiveness of the proposed framework.
Keywords:
Machine Learning: Machine Learning
Machine Learning: Neural Networks
Machine Learning: Unsupervised Learning
Machine Learning: Deep Learning