| Proj No. | A3193-251 |
| Title | Development of synthetic aperture radar (SAR) image processing using deep learning algorithms |
| Summary | Over the past decade, synthetic aperture radar (SAR) is a popular research domain for remote sensing in military and civil applications. SAR image processing encompasses various methodologies, including denoising, classification and recognition, detection, and segmentation. Nowadays, leveraging the advancements in image feature extraction deep learning (DL) models demonstrate exceptional efficacy and find extensive application in SAR image processing, such as GAN-based SAR data augmentation, DNN-based SAR image denoising, classification and recognition. The main purpose of this project is to explore a novel DL-based model to enhance SAR image processing outcomes across diverse SAR challenges, such as SAR small sample size issues, low signal-to-noise ratio (SNR) issues, and classification tasks. Students are encouraged to explore different perspectives and gain insights about SAR and choose one for research. Matlab or Python programming will be used to generate numerical results for this project. |
| Supervisor | A/P Teh Kah Chan (Loc:S2 > S2 B2A > S2 B2A 03, Ext: +65 67905365) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Centre for Information Sciences & System (CISS) (Loc: S2-B4b-05) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |