CenterNet detects object as a point and predicts the location offsets of the center points and the size of the bounding boxes. FCOS classifies each pixel on the feature map, which is inspired by the semantic segmentation methods. A center-ness strategy is proposed for suppressing low-quality detected bounding boxes. However, original anchor-free methods such as FCOS cannot precisely detect composite objects in complex environments without stronger feature representation and a suitable constraint strategy of predicted boxes. We have previously proposed an attention-guided network (ADNet) [28] for PSSs detection. ADNet is an anchor-based method and requires setting of hyper-parameters related to anchors. In contrast, anchor-free methods completely give up the guidance of  
Figure 1: Samples of PSSs in different regions. The previous four images show the PSSs in urban areas. The last two images show the PSSs in remote regions. anchors and can achieve promising performance. This gives them more potential in object detection tasks. Therefore, we studied the PSSs detection based on the anchor-free method. In this article, we propose a feature enhanced network (FENet) based on FCOS for PSSs detection. The model can capture more semantic information by modeling foreground objects based on enhanced feature modules. In addition, the model optimizes the loss function to filter background information. The model can avoid hyper-parameters and complicated computation related to anchors by eliminating anchor boxes. For PSSs detection, FENet enhances the ability of the model to obtain critical information and improves localization accuracy. Meanwhile, a PSSs dataset is also presented for composite object detection, which collects 1685 images from GF images in Beijing-Tianjin-Hebei regions. The main contributions of our work are summarized as follows:  
1. We propose a feature enhanced network (FENet) for PSSs detection. The proposed method can improve the performance of PSSs detection in RSIs and effectively avoid the influence of negative samples. Compared with other object detection methods, our proposed method can locate the objects precisely without complex computation related to anchors. This simple anchor-free method also provides a new idea for object detection. 2. An enhanced feature module (EFM) is proposed to enlarge the receptive field in high-level layers and enhance discrimination of features. EFM contains two parts: one is a multi-scale local attention (MSLA) module for extracting multi-scale features, and the other is a channel attention-guided unit (CAU) for re-weighting features to obtain global attention and improve the semantic consistency among multiscale features. Through critical information extraction of high-level layers and further feature fusion, EFM can improve the classification and localization during PSSs detection. 3. A context-aware strategy and complete _IoU_ (CIoU) loss are introduced to our network for further optimizing predicted bounding boxes. The context-aware strategy can make full use of foreground information and generate more positive samples. The CIoU loss considers the relationship between predicted boxes and ground-truth boxes in many cases and achieves faster regression of bounding boxes. These strategies are suitable for anchor-free methods and can effectively predict positive samples while ignoring the negative samples. 4. We build a PSSs dataset for composite object detection. This dataset is based on GF satellites with 2 m resolution and includes 1685 annotated images. The PSSs dataset provides a benchmark for future composite object detection. The remainder of this study is organized as follows: Section 2 introduces the proposed method in detail, including the basic feature extractor, enhanced feature module, context-aware strategy, and multitask loss function. The experiments and results are presented and analyzed in Section 3. Section 4 discusses the results and the generalization ability of our method. Finally, the conclusions of this article are presented in Section 5.