A Siamese convolutional neural network with high–low level feature fusion for change detection in remotely sensed images

ABSTRACT

Automatic change detection is an important and difficult task in the field of remote sensing. In this study, a deep Siamese convolutional network based on the fusion of high- and low-level features is proposed for change detection in remote sensing images. Given that low-level features correspond to low-order ones (e.g., texture) that are sensitive to change and that high-level features can accurately reflect image category information (e.g., semantic information), we fuse these features to enhance the abstractness and robustness of the extracted features in the change detection framework. The whole system is end-to-end and does not require any pre- or post-processing. Experimental results on three datasets show that our method is superior to other advanced methods by adding a high- and low-level fusion framework.

Additional information

Funding

This work was partly supported in part by the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, under Project 18R01, and in part by the National Natural Science Foundation of China under Project No. 41901265; National Natural Science Foundation of China [92038301]; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing [18R01].