Difference Guidance Learning With Feature Alignment for Change Detection

Change detection (CD) in remote sensing aims at identifying changes of specific categories from multitemporal images acquired at different moments of a given scene. Due to seasonal alteration and light variation, there are always pseudo-changes hard to be recognized. To this end, we propose a difference guidance learning way to mitigate the effects of pseudo-change, which benefits capturing more discriminative information and identifying real changes. Specifically, it combines difference information with fused features in a guidance way and generates discriminative features in multiple scales. Besides that, feature alignment is conducted in the highest stage to learn feature correlations between bitemporal images, which benefits identifying semantic changes by information exchange. Therefore, the proposed method is named feature alignment and difference guidance network (FADG-Net). Furthermore, a set of convolutional layers with different receptive field sizes is also utilized to capture spatial information across different scales and enhance texture features accordingly. Tested on three public CD datasets, the effectiveness of the proposed FADG-Net is verified, where pseudo-change problem is mitigated and our method is superior to other comparison methods.