Investigation of Image Change Detection Based on Multiscale and Multiplex Technologies

In order to provide better geographic information services on time, a detection technique on the variation of remote sensing image has been investigated in this paper. Based on multiscale feature multiplex, a consistent regularization method together with a semi-supervised strategy has been proposed, which can solve the problems of unclear change edges and the issue of missing detection of very small targets. By constructing a learning training model of detection network, the performances have been enhanced with limited labeled data and effectively handle change detection tasks in complex scenes. The network consists of two parts. One part is the supervised multiscale feature fusion and multiplex, which enhances feature expression capabilities using channel and spatial attention mechanisms. We extract variation features through complex multiscale feature fusion and reuse strategies to alleviate unclear change edges and the omission of very small targets. The other part is the consistency regularization method for semi-supervised learning model, which adopts an alternating training among models. The prediction differences have been minimized, then updated model parameters. This constructed model of detection network has been verified at different labeling ratios on the datasets such as Wuhan university, China, building change detection and google earth imagery Guangzhou, China, change detection, etc. The experimental results show that this proposed technique demonstrates the best overall performance in complex situations with small dataset labeling ratios among existing detection methods.