Feature-Band-Based Unsupervised Hyperspectral Underwater Target Detection Near the Coastline.

With the improvement of imaging equipment, hyperspectral underwater target detection (HUTD) has raised much interest in recent years. The existing HUTD methods do not fully use spectral characteristics and need prior information about targets. Besides, the detection performance lacks verification in natural scenarios. In this article, the authors propose a feature-band-based unsupervised underwater target detection method (FBUD), which aims at finding the optimal feature bands to identify the underwater target near the coastline. Specifically, the normalized difference water index (NDWI) and unmixing technique are adopted to find the target and background pixels. Then, the spectral difference between the target and background is used to find the feature bands. With a simple and fast math operation of the feature bands, the probability map of the underwater target can be easily obtained. Besides, a new unmanned aerial vehicle (UAV)-borne hyperspectral image (HSI) dataset named HNU-UTD is built for underwater target detection in real-world scenes. Experimental results obtained with the HNU-UTD dataset confirm the accuracy and effectiveness of the proposed detection method, which even outperforms supervised detection methods.