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

## TL;DR

This paper investigates effective transfer learning strategies for SAR target recognition using deep CNNs, focusing on what, where, and how to transfer knowledge from source tasks and data to improve performance.

## Contribution

It analyzes transfer learning issues specific to SAR images and proposes a transitive transfer method with domain adaptation to enhance recognition accuracy.

## Key findings

- Transfer learning from natural images is less effective for SAR.
- Identifies optimal network layers and source tasks for SAR transfer.
- Proposes a multi-source domain adaptation approach for SAR recognition.

## Abstract

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.