Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights

• URL: http://arxiv.org/abs/2310.17126v1
• Date: Thu, 26 Oct 2023 03:52:54 GMT
• Title: Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights
• Authors: Morteza Karimzadeh, Rafael Pires de Lima
• Abstract summary: We compare the performance of deep learning models trained from scratch and pretrained models that we fine-tune for this purpose. Our results show that pre-trained models lead to better results, especially on test samples from the melt season.
• Score: 1.6969743728555278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes. One such case is sea ice, which is highly dynamic and rapidly changes as a result of the combined effect of wind, temperature, and ocean currents. Therefore, frequent mapping of sea ice is necessary to ensure safe marine navigation. However, there is a general shortage of expert-labeled data to train deep learning algorithms. Fine-tuning a pre-trained model on SAR imagery is a potential solution. In this paper, we compare the performance of deep learning models trained from scratch using randomly initialized weights against pre-trained models that we fine-tune for this purpose. Our results show that pre-trained models lead to better results, especially on test samples from the melt season.

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