Paper Info
Title
What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs
Abstract
Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing 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 pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, 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 multisource data with domain adaptation is proposed in this article 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.
Year
DOI
Venue
2020
10.1109/TGRS.2019.2947634
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Synthetic aperture radar,Task analysis,Target recognition,Optical sensors,Remote sensing,Optical imaging,Radar polarimetry
Journal
58
Issue
ISSN
Citations 
4
0196-2892
4
PageRank 
References 
Authors
0.44
0
3
Name
Order
Citations
PageRank
Zhongling Huang1443.35
Zongxu Pan2748.13
Bin Lei3266.38