# What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs

arXiv: https://arxiv.org/abs/1906.01379 · HTML: https://mlsanity.com/paper/1906.01379/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [Physically Explainable CNN for SAR Image Classification](https://mlsanity.com/paper/2110.14144/) (2021)
- [Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition](https://mlsanity.com/paper/2304.03550/) (2023)
- [Physics Inspired Hybrid Attention for SAR Target Recognition](https://mlsanity.com/paper/2309.15697/) (2023)
- [X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images](https://mlsanity.com/paper/2407.19436/) (2024)
- [Physics-Guided Detector for SAR Airplanes](https://mlsanity.com/paper/2411.12301/) (2024)
- [Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR](https://mlsanity.com/paper/2408.03627/) (2024)
- [SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and A Progressive Learning Strategy for Downstream Tasks](https://mlsanity.com/paper/2507.18743/) (2025)
- [SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition](https://mlsanity.com/paper/2606.31668/) (2026)
- [Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar](https://mlsanity.com/paper/2301.03589/) (2023)
- [MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images](https://mlsanity.com/paper/2308.14613/) (2023)
- [PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection](https://mlsanity.com/paper/2603.14886/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated dataset in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in SAR image interpretation. Transfer learning provides an effective way to solve this problem by borrowing the knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pre-trained with a large-scale natural image dataset, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR application because of the prominent discrepancy between SAR and optical images. In this paper, we attempt to discuss three issues that are seldom studied before in detail: (1) what network and source tasks are better to transfer to SAR targets, (2) in which layer are transferred features more generic to SAR targets and (3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively.