Chen, J.Cheng, D.Jiang, J.Yu, Z.Zhang, S.2025-12-182025-12-182023Proceedings / ICIP ... International Conference on Image Processing, 2023, pp.855-85997817281983541522-4880https://hdl.handle.net/11541.2/37391It is challenging to detect objects in remote sensing images due to there being a large number of objects with few available features and a lot of background noise. Most existing methods ignore a large amount of background noise. In this paper, we propose an end-to-end based network model, Semantic feature Enhancement Model with a Fully Convolutional head prediction Network, referred to as SEM-FCNet, to reduce the effect of background noise in remote sensing images detection. First of all, SEM-FCNet consists of a new semantic feature enhancement module to enhance the semantic features of small objects and reduce noise interference by fusing attention mechanism. Then, SEM-FCNet utilizes a fully convolutional head prediction network to detect multiple objects by extracting their location information. The experiments on two famous remote sensing image datasets, NWPU VHR-10 and HRSC, demonstrate the performance of the proposed SEM-FCNet model over the existing remote sensing image detection methodsenCopyright 2023 IEEE Access Condition Notes: Accepted manuscript available open accessattention mechanismconvolutional neural network (CNN)object detectionremote sensing imageConference paper10.1109/ICIP49359.2023.102231392-s2.0-85180804426