  In the field of deep-sea exploration, sonar is presently the only efficient
long-distance sensing device. The complicated underwater environment, such as
noise interference, low target intensity or background dynamics, has brought
many negative effects on sonar imaging. Among them, the problem of nonlinear
intensity is extremely prevalent. It is also known as the anisotropy of
acoustic sensor imaging, that is, when autonomous underwater vehicles (AUVs)
carry sonar to detect the same target from different angles, the intensity
variation between image pairs is sometimes very large, which makes the
traditional matching algorithm almost ineffective. However, image matching is
the basis of comprehensive tasks such as navigation, positioning, and mapping.
Therefore, it is very valuable to obtain robust and accurate matching results.
This paper proposes a combined matching method based on phase information and
deep convolution features. It has two outstanding advantages: one is that the
deep convolution features could be used to measure the similarity of the local
and global positions of the sonar image; the other is that local feature
matching could be performed at the key target position of the sonar image. This
method does not need complex manual designs, and completes the matching task of
nonlinear intensity sonar images in a close end-to-end manner. Feature matching
experiments are carried out on the deep-sea sonar images captured by AUVs, and
the results show that our proposal has preeminent matching accuracy and
robustness.
