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SAR Image Classification: A Comprehensive Study and Analysis
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DOI：10.1109/ACCESS.2022.3151089.png)
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Abstract
En 中文
Deep learning has obtained wide attention in various fields enabling systems to derive essential information from digital inputs. Lately, the use of deep learning in remote sensing applications has also been motivated and applied, wherein considerable improvements in the results are witnessed. Synthetic aperture radar images have been used in various earth observation systems because of their all-day imaging capacity and self-illuminating nature. Various works concentrating on extracting meaningful information from SAR data for various other applications have been proposed in the literature. Classification of SAR images has been one of the utmost steps in numerous SAR applications. Therefore, this work focuses on studying several existing techniques that use deep learning for SAR image classification by examining the architectures involved. Based on the study, crucial observations are made, highlighting the merits and demerits of several approaches, allowing researchers to better understand how the methods can impact the performance of the deep learning models for SAR image classification in the future. Potential hybrid models for the classification of SAR images are also presented in this paper.
Keywords:
Radar polarimetry
Synthetic aperture radar
Deep learning
Radar imaging
Convolutional neural networks
Feature extraction
Remote sensing
Deep learning
image classification
image processing
remote sensing
synthetic aperture radar
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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