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

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.

Authors

• Puzhao Zhang
• Maoguo Gong School of Electronic Engineering, Xidian University, Xi’an 710071, China Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xi’an 710071, China
• Hui Zhang
• Jia Liu Xi'an Jiaotong University, Xi'an, China

Keywords

• Machine Learning: Deep Learning
• Machine Learning: Machine Learning
• Machine Learning: Neural Networks
• Machine Learning: Unsupervised Learning