A Robust LCSE-ResNet for Marine Man-made Target Classification Based on Optical Remote Sensing Imagery

Abstract

It is of great need to accurately locate vessels and oil platforms because huge numbers of these marine man-made targets can cause oil spills or illegal invasion problems. Many different types of marine targets in the complex marine environment make it very difficult to deal with the target detection and classification process accurately. This paper proposes a robust Laplacian of Gaussian operator connected domain controller squeeze excitation residual network (LCSE-ResNet) for marine man-made target classification based on optical remote sensing imagery. Vessel and oil platform candidate regions in remote sensing images are extracted by the Laplacian of Gaussian (LoG) operator and the connected domain controller, which are input into the classification neural networks in the following. It is a whole End-to-End structure from original remote sensing images to the precise target information. That clouds, ripples, and the other man-made target disturbances can be excluded effectively means the better robust character in the actual application. Some shape and size features of vessels and oil platforms are considered in the LCSE-ResNet structure, which improves the explanation of the final results. Experimental results demonstrate that the proposed LCSE-ResNet can effectively detect marine man-made targets and effectively distinguish vessels and oil platforms.