# N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/n-yolo-a-sar-ship-detection-using-noise-classifying-and-complete-target/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Gang Tang,Yichao Zhuge,Christophe Claramunt,Shaoyang Men |
| Citations | 99 |
| DOI | 10.3390/rs13050871 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2072-4292/13/5/871/pdf?version=1614651173 |
| OpenAlex ID | https://openalex.org/W3134517928 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Gang Tang](https://scholariq.org/researchers/gang-tang/)

## Paper primary topic

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

## Paper topics

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Advanced SAR Imaging Techniques](https://scholariq.org/topics/advanced-sar-imaging-techniques/)
- [Synthetic Aperture Radar (SAR) Applications and Techniques](https://scholariq.org/topics/synthetic-aperture-radar-sar-applications-and-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.