  With the continuous development of underwater vision technology, more and
more remote sensing images could be obtained. In the underwater scene, sonar
sensors are currently the most effective remote perception devices, and the
sonar images captured by them could provide rich environment information. In
order to analyze a certain scene, we often need to merge the sonar images from
different periods, various sonar frequencies and distinctive viewpoints.
However, the above scenes will bring nonlinear intensity differences to the
sonar images, which will make traditional matching methods almost ineffective.
This paper proposes a non-linear intensity sonar image matching method that
combines local feature points and deep convolution features. This method has
two key advantages: (i) we generate data samples related to local feature
points based on the self-learning idea; (ii) we use the convolutional neural
network (CNN) and Siamese network architecture to measure the similarity of the
local position in the sonar image pair. Our method encapsulates the feature
extraction and feature matching stage in a model, and directly learns the
mapping function from image patch pairs to matching labels, and achieves
matching tasks in a near-end-to-end manner. Feature matching experiments are
carried out on the sonar images acquired by autonomous underwater vehicle (AUV)
in the real underwater environment. Experiment results show that our method has
better matching effects and strong robustness.
