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 interdomain differences. However, current approaches often overlook the focused attention on foreground features, and the differences between the foreground and the background in remote sensing images implicitly diminish the characteristics of the foreground. To address this challenge, we propose a foreground instance enhancement network (FIE-Net) to balance the differences between the foreground and the background in remote sensing images, while enhancing the alignment and application of foreground features. The FIE-Net cooperates with the foreground-focused multigranularity 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 toward the foreground focus during the multigranularity feature alignment process, through foreground-focus 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. The code is released at https://github.com/Lab-PANbin/