SEM-FCNET: Semantic feature enhancement and fully convolutional network model for remote sensing object detection
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(Published version)
Date
2023
Authors
Chen, J.
Cheng, D.
Jiang, J.
Yu, Z.
Zhang, S.
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Conference paper
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Proceedings / ICIP ... International Conference on Image Processing, 2023, pp.855-859
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30th IEEE International Conference on Image Processing, ICIP 2023 (8 Oct 2023 - 11 Oct 2023 : Kuala Lumpur)
Abstract
It 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 methods
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Copyright 2023 IEEE
Access Condition Notes: Accepted manuscript available open access