Remote Sensing Change Detection Based on Feature Fusion and Attention Network

Abstract

Change detection is one of the essential tasks in remote sensing,which is usually regarded as a pixel-level classification problem.In recent years,deep neural networks have also been widely used in the change detection task due to their powerful hierarchical representation of bi-temporal images.A feature fusion and attention network (FFAN) is proposed based on neural encoder-fusion-decoder framework.It integrates features generated by encoder with the bi-temporal difference feature enhanced by attention mechanism,to better capture the bi-temporal change information.In particular,bi-temporal features enhanced by attention mechanism can significantly enhance the propagation of change information in the intermediate layers of deep networks,which adaptively recalibrates the change activation in FFAN by explicitly modeling the interdependence of bi-temporal inputs.Experiments conducted on open-source dataset demonstrate that,compared with existing methods,FFAN obtains better performance.

Keywords

• change detection|remote sensing|deep learning|attention mechanism|feature fusion