Underwater Target Recognition via Cayley-Klein Measure and Shape Prior Information in Hyperspectral Imaging

Abstract

Underwater target detection plays a vital role in various application scenarios, ranging from scientific research to military and industrial operations. In this paper, a detection method via the Cayley–Klein measure and a prior information of shape is proposed for the issue of hyperspectral underwater target identification. Firstly, by analyzing the data features of underwater targets and backgrounds, a background suppression algorithm based on Cayley–Klein measure is developed to enhance the differentiation between underwater targets and backgrounds. Then, a local peak-based algorithm is designed to discriminate potential underwater target points based on the local peak features of underwater targets. Finally, pseudo-target points are eliminated based on the priori shape information of underwater targets. Experiments show that the algorithm proposed is efficient and can effectively detect underwater targets from hyperspectral images.