Image-based seabed classification: what can we learn from terrestrial remote sensing?

Maps that depict the distribution of substrate, habitat or biotope types on the seabed are in increasing demand by marine ecologists and spatial planners, underpinning decision making in relation to marine spatial planning and marine protected area network design. Yet, the science discipline of image-based seabed mapping has not fully matured and rapid progress is needed to improve the reliability and accuracy of maps. To speed up the process we have conducted a literature review of common practices in terrestrial image classification based on remote sensing data, a related discipline, albeit with a larger scientific community and longer history. We identified the following key elements of a mapping workflow: (i) Data pre-processing, (ii) Feature extraction, (iii) Feature selection, (iv) Classification, (v) Post-classification enhancements, and (vi) Evaluation of classification performance. Insights gained from the review served as a baseline against which recent seabed mapping studies were compared. In this way we identified knowledge gaps and propose modifications to the mapping workflow. A main concern in current seabed mapping practice is that a large amount of often correlated predictor features is extracted, creating a multidimensional feature space. To effectively fill this space with an appropriate amount of training samples is likely to be impossible. Hence, it is necessary to reduce the dimensionality of the feature space via data transformation [e.g. principal component analysis (PCA)] or feature selection and remove correlated features. We propose to make dimensionality reduction an integral part of any mapping workflow. We also suggest to adopt recommendations for accuracy assessment originally drawn up for terrestrial land cover mapping. These include the publication of two or more measures of accuracy including overall and class-specific metrics, publication of associated confidence limits and the provision of the error matrix.