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SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images
| Content Provider | MDPI |
|---|---|
| Author | Su, Nan He, Jiayue Yan, Yiming Zhao, Chunhui Xing, Xiangwei |
| Copyright Year | 2022 |
| Description | Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels. |
| Starting Page | 442 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14030442 |
| Journal | Remote Sensing |
| Issue Number | 3 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-18 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Synthetic Aperture Radar (sar) Ship Detection Small Target Spatial Features Spatial Information Integration Network (sii-net) |
| Content Type | Text |
| Resource Type | Article |
