Hyperspectral subpixel target detection based on interaction subspace model

Sun, Shengyin; Liu, Jun; Sun, Siyu

doi:10.1016/j.patcog.2023.109464

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kernel Method [3], Sparse Representation Model [4], Discriminant Subspace Analysis [5,6] and Deep Learning Model [7]. For instance, a hyperspectral image anomaly detection method based on Support Vector Data Description (SVDD) was proposed by Li et al [8], which can describe the image background depending on a few pixels without requiring the prior knowledge of data distribution.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Sun

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract