  Application of underwater robots are on the rise, most of them are dependent
on sonar for underwater vision, but the lack of strong perception capabilities
limits them in this task. An important issue in sonar perception is matching
image patches, which can enable other techniques like localization, change
detection, and mapping. There is a rich literature for this problem in color
images, but for acoustic images, it is lacking, due to the physics that produce
these images. In this paper we improve on our previous results for this problem
(Valdenegro-Toro et al, 2017), instead of modeling features manually, a
Convolutional Neural Network (CNN) learns a similarity function and predicts if
two input sonar images are similar or not. With the objective of improving the
sonar image matching problem further, three state of the art CNN architectures
are evaluated on the Marine Debris dataset, namely DenseNet, and VGG, with a
siamese or two-channel architecture, and contrastive loss. To ensure a fair
evaluation of each network, thorough hyper-parameter optimization is executed.
We find that the best performing models are DenseNet Two-Channel network with
0.955 AUC, VGG-Siamese with contrastive loss at 0.949 AUC and DenseNet Siamese
with 0.921 AUC. By ensembling the top performing DenseNet two-channel and
DenseNet-Siamese models overall highest prediction accuracy obtained is 0.978
AUC, showing a large improvement over the 0.91 AUC in the state of the art.
