Publication Date:
2018-02-23
Description:
Hyperspectral seafloor surveys using airborne or spaceborne sensors are generally limited to shallow coastal
areas, due to the requirement for target illumination by sunlight. Deeper marine environments devoid of sunlight
cannot be imaged by conventional hyperspectral imagers. Instead, a close-range, sunlight-independent hyperspectral
survey approach is required. In this study, we present the first hyperspectral image data from the deep
seafloor. The data were acquired in approximately 4200m water depth using a new Underwater Hyperspectral
Imager (UHI) mounted on a remotely operated vehicle (ROV). UHI data were recorded for 112 spectral bands
between 378 nm and 805 nm, with a high spectral (4 nm) and spatial resolution (1mm per image pixel). The
study area was located in a manganese nodule field in the Peru Basin (SE Pacific), close to the DISCOL
(DISturbance and reCOLonization) experimental area. To test whether underwater hyperspectral imaging can be
used for detection and mapping of mineral deposits in potential deep-sea mining areas, we compared two supervised
classification methods, the Support Vector Machine (SVM) and the Spectral Angle Mapper (SAM). The
results show that SVM is superior to SAM and is able to accurately detect nodule surfaces. The UHI therefore
represents a promising tool for high-resolution seafloor exploration and characterisation prior to resource exploitation.
Repository Name:
EPIC Alfred Wegener Institut
Type:
Article
,
peerRev
Format:
application/pdf
