Significant Feature Elimination and Sample Assessment for Remote Sensing Small Objects's Detection

In recent years, small object detection has remained challenging in remote sensing tasks. First, small objects inherently have fewer pixels, making them susceptible to interference from prominently featured larger objects during feature extraction. Second, existing detection methods solely based on the intersection over union (IOU) loss are disadvantageous for small object detection and fail to leverage the rich prior information in remote sensing images. Based on these observations, we propose a significant feature elimination and sample assessment network for small object detection called SESA-Net, based on the Facet derivative model. SESA-Net introduces prior information to the network through the directional derivatives characteristic of remote sensing images. The overall network comprises the adaptive derivative mask (ADM) module and sample importance assessment (SIA) strategy. The ADM module eliminates significant responses from shallow large objects, directing the network's focus toward the features of shallow small objects. The SIA strategy addresses the limitations of the IOU loss function using high-quality positive samples generated by ADM to provide an evaluation strategy for different positive samples of small objects. This enables the network to focus more on high-quality positive samples, thereby improving the accuracy of small object detection. The effectiveness of the proposed algorithm has been validated on multiple datasets.