Hyperspectral target detection is widely used in both military and civilian fields.In practical applications, how to select a low-correlation and representative band subset to reduce redundancy is worth discussing.However, most of the existing band selection (BS) methods usually select bands according to the statistics or correlation, which neglect Being unvaccinated and having a contact history increased the risk of measles infection during an outbreak: a finding from measles outbreak investigation in rural district of Ethiopia the spectral characteristics of the desired target and are not specially designed for target detection.
Therefore, this article proposed a novel BS method, called constrained-target BS with subspace partition (CTSPBS), to select an optimal subset with low internal correlation and strong target representability for the target detection task.By using a specially designed subspace partition method based on correlation distance (CDSP), CTSPBS divides the hyperspectral bands into several unrelated subspaces.Then, according to certain constrained-target band prioritization (BP) criteria, the band with the Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field highest priority in each subset is selected to form the optimal subset for a specific target.
Correspondingly, two versions of the proposed method, minimum variance BS with CDSP (CDSP_MinV) and minimum variance BS with CDSP (CDSP_MaxV), are derived to implement CTSPBS.Extensive experiments on three public hyperspectral datasets demonstrate that the proposed method exhibit more robust and effective performance than several state-of-the-art methods.Finally, this article focuses on the difficulty of marine benthos detection in mariculture application and proves the feasibility of the proposed method.