Abstract
In recent years, convolutional neural networks (CNNs) have drawn considerable attention for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR data analysis that have been tackled by CNNs are systematically reviewed, such as automatic target recognition, land use and land cover classification, segmentation, change detection, object detection, and image denoising. Special emphasis has been given to practical techniques such as data augmentation and transfer learning. Complex-valued CNNs, which have been introduced to exploit phase information embedded in SAR complex images, have also been extensively reviewed. To conclude this review paper, open challenges and future research directions are highlighted.
| Original language | English |
|---|---|
| Pages (from-to) | 18-42 |
| Number of pages | 25 |
| Journal | IEEE Aerospace and Electronic Systems Magazine |
| Volume | 37 |
| Issue number | 5 |
| Early online date | 30 Sept 2021 |
| DOIs | |
| Publication status | Published - May 2022 |
Bibliographical note
Publisher Copyright:© 1986-2012 IEEE.
Keywords
- Automatic Target Recognition
- Change Detection
- Complex-Valued CNN
- Convolutional Neural Network
- Data Augmentation
- Deep Learning
- Land Use and Land Cover Classification
- Object Detection
- Segmentation
- Synthetic Aperture Radar
- Transfer Learning
ASJC Scopus subject areas
- Aerospace Engineering
- Space and Planetary Science
- Electrical and Electronic Engineering
