Radon single-pixel flying target classification via texture-fused lightweight differentiable operators
Hubin Ling, Bingzhang Hu, Dongfeng Shi, Yinbo Huang, Yingjian Wang · 2025
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Description
Hubin Ling and co-authors classify flying targets from single-pixel images in this 2025 paper. Radon single-pixel imaging samples fast and works over long range. That suits bird monitoring and counter-drone tasks such as airport protection.
Lowering the sampling rate speeds imaging further, which helps track fast aerial objects. The cost is poorer image quality, which makes recognition harder. Most current classifiers for this data use small, shallow networks with weak discrimination. The team fuse texture cues into light, differentiable operators instead. Their design aims to keep computing simple while sharpening class calls. Better calls from sparse data could improve airspace safety systems. Airport and border teams are the clear first users.
Catalog record
| CREATOR | Hubin Ling, Bingzhang Hu, Dongfeng Shi, Yinbo Huang, Yingjian Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |