FIE-Net: Foreground Instance Enhancement Network for Domain Adaptation Object Detection in Remote Sensing Imagery

Domain adaptation methods can mitigate performance degradation in remote sensing image object detection that arises from inter-domain differences. However, current approaches often overlook the focused attention on foreground features, and the differences between foreground and background in remote sensing images implicitly diminishes the characteristics of the foreground. To address this challenge, we propose a foreground instance enhancement network (FIE-Net) to balance the differences between foreground and background in remote sensing images, while enhancing the alignment and application of foreground features. The FIE-Net cooperates the foregroundfocused multi-granularity feature alignment (FMA) module with the label filtering and application (LFA) module, progressively focusing on the salient foreground features during the processes of feature alignment and label application. Specifically, FMA directs feature alignment towards the foreground focus during the multi-granularity feature alignment process, through foregroundfocus perception attention and instance-centered emphasis approach. LFA balances the foreground and background difference information contained in labels, through the hybrid threshold label filtering method and the progressive label switching strategy. The experimental results indicate superior performance and generalization capabilities of our proposed FIE-Net in multiple remote sensing adaptation scenarios. Code is released at https://github.com/Lab-PANbin/.