Few-Shot Transfer Learning for SAR Image Classification without Extra SAR Samples

Yihua Tan

Abstract:

Deep learning-based synthetic aperture radar (SAR)
image classification is an open problem when training samples are
scarce. Transfer learning-based few-shot methods are effective to
deal with this problem by transferring knowledge from the electro–
optical (EO) to the SAR domain. The performance of such methods
relies on extra SAR samples,such as unlabeled novel class’ssamples
or labeled similar classes samples. However, it is unrealistic to
collect sufficient extra SAR samples in some application scenarios,
namely the extreme few-shot case. In this case, the performance
of such methods degrades seriously. Therefore, few-shot methods
that reduce the dependence on extra SAR samples are critical.
Motivated by this, a novel few-shot transfer learning method for
SAR image classification in the extreme few-shot case is proposed.
We propose the connection-free attention module to selectively
transfer features shared between EO and SAR samples from a
source network to a target network to supplement the loss of
information brought by extra SAR samples. Based on the Bayesian
convolutional neural network, we propose a training strategy for
the extreme few-shot case, which focuses on updating important
parameters, namely the accurately updating important parameters.
The experimental results on the three real-SAR datasets
demonstrate the superiority of our method.