Automated Activity Estimation of the Cold-Water Coral Lophelia pertusa by Multispectral Imaging and Computational Pixel Classification

Hongbo Liu, Janina v. Büscher, Kevin Köser, Jens Greinert, Hong Song, Ying Chen, Timm Schoening

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
11 Downloads (Pure)


The cold-water coral Lophelia pertusa builds up bioherms that sustain high biodiversity in the deep ocean worldwide. Photographic monitoring of the polyp activity represents a helpful tool to characterize the health status of the corals and to assess anthropogenic impacts on the microhabitat. Discriminating active polyps from skeletons of white Lophelia pertusa is usually time consuming and error prone due to their similarity in color in common red–green–blue (RGB) camera footage. Acquisition of finer-resolved spectral information might increase the contrast between the segments of polyps and skeletons, and therefore could support automated classification and accurate activity estimation of polyps. For recording the needed footage, underwater multispectral imaging systems can be used, but they are often expensive and bulky. Here we present results of a new, lightweight, compact, and low-cost deep-sea tunable LED-based underwater multispectral imaging system (TuLUMIS) with eight spectral channels. A branch of healthy white Lophelia pertusa was observed under controlled conditions in a laboratory tank. Spectral reflectance signatures were extracted from pixels of polyps and skeletons of the observed coral. Results showed that the polyps can be better distinguished from the skeleton by analysis of the eight-dimensional spectral reflectance signatures compared to three-channel RGB data. During a 72-h monitoring of the coral with a half-hour temporal resolution in the laboratory, the polyp activity was estimated based on the results of the multispectral pixel classification using a support vector machine (SVM) approach. The computational estimated polyp activity was consistent with that of the manual annotation, which yielded a correlation coefficient of 0.957.
Original languageEnglish
Pages (from-to)141-154
Number of pages14
JournalJournal of Atmospheric and Oceanic Technology
Issue number2
Publication statusPublished (in print/issue) - 1 Feb 2021


  • Ocean
  • In situ oceanic observations
  • Classification
  • Spectral analysis/models/distribution
  • Support vector machines


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