Data Augmentation for Ship Detection using Kompsat-5 Images and Deep Learning Model
| Autor: | Jae Young Chang, Kwan-Young Oh, Seung-Jae Lee, Kwang-Jae Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Synthetic aperture radar 010504 meteorology & atmospheric sciences Computer science business.industry Deep learning Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Training (meteorology) 02 engineering and technology 01 natural sciences Clutter Computer vision Satellite Artificial intelligence business computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | IGARSS |
| Popis: | This paper proposes a useful scheme to augment the amount of training database (DB) for ship detection using Korea multi-purpose satellite–5 (KOMPSAT–5) images and deep learning. The proposed scheme utilizes electromagnetic numerical analysis technique to generate SAR chips of ship targets. In addition, two sea clutter models are adopted to simulate realistic SAR patches containing various SAR chips. Then, the simulated SAR patches are directly used to train single shot multi-box detector (SSD) model, leading to the improvement of ship detection performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|