  Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise,
which makes it very difficult to perform target recognition in SAR images.
Inspired by great success of very deep convolutional neural networks (CNNs),
this paper proposes a robust feature extraction method for SAR image target
classification by adaptively fusing effective features from different CNN
layers. First, YOLOv4 network is fine-tuned to detect the targets from the
respective MF SAR target images. Second, a very deep CNN is trained from
scratch on the moving and stationary target acquisition and recognition (MSTAR)
database by using small filters throughout the whole net to reduce the speckle
noise. Besides, using small-size convolution filters decreases the number of
parameters in each layer and, therefore, reduces computation cost as the CNN
goes deeper. The resulting CNN model is capable of extracting very deep
features from the target images without performing any noise filtering or
pre-processing techniques. Third, our approach proposes to use the
multi-canonical correlation analysis (MCCA) to adaptively learn CNN features
from different layers such that the resulting representations are highly
linearly correlated and therefore can achieve better classification accuracy
even if a simple linear support vector machine is used. Experimental results on
the MSTAR dataset demonstrate that the proposed method outperforms the
state-of-the-art methods.
