SDCDNet: A Semi-Dual Change Detection Network Framework With Super-Weak Label for Remote Sensing Image

Abstract

Most current change detection methods require a large amount of labeled data to train huge parameters. To break this limitation, this paper proposes a novel semi-supervised learning framework for remote sensing change detection, named a semi-dual change detection network (SDCDNet). The SDCDNet consists of a dual shared network and dual branching networks. The dual shared network is designed to exploit the full potential of the data, and the dual branching network is proposed to differentiate the kinds of annotated data and eliminate the disturbance between different types of data. In addition, the adaptive weighting module (AWM) enhances the features of weak branching, and the mask constraint module (MCM) is proposed to increase the ability of the network to extract foreground features. To solve the complex problem of data labeling, a patch-based weak label construction method is proposed to build super-weak labels. Experiments show that the proposed SDCDNet achieves excellent results on two remote sensing image change detection datasets.