Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8866621 | Remote Sensing of Environment | 2018 | 12 Pages |
Abstract
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 4200â¯m 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 378â¯nm and 805â¯nm, with a high spectral (4â¯nm) and spatial resolution (1â¯mm 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.
Keywords
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Ines Dumke, Stein M. Nornes, Autun Purser, Yann Marcon, Martin Ludvigsen, Steinar L. Ellefmo, Geir Johnsen, Fredrik Søreide,