Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image

Theoretically, spectral separability will greatly affect the accuracy of multispectral classification. This study aims to understand the relationship between the inter-class spectral separability and the accuracy of benthic habitat classification using a WorldView-2 multispectral image. The study area for this research is Kemujan Island, Jepara Regency, Central Java Province, Indonesia. The datasets used are sunglint-corrected bands, Principle Component Analysis (PCA)-derived bands, vegetation indices, and filter occurrence bands. Benthic habitat field data were obtained through a photo-transect survey technique and were used to construct nine levels of benthic habitat hierarchical classification schemes. We used maximum likelihood (ML) and random forest (RF) as the classification algorithms. Spectral separability was calculated using the Jeffries-Matusita separability analysis algorithm. The results from both RF and ML showed that the increased number of class pairs with spectral separability less than 1.0 (S-<1.0) decreased the OA and an increased number of class pairs with S>1.0-1.9 increased the OA. Especially for scheme Level 1 with the greatest number of classes, an increased number of class pairs with S->1.9 to is required to improve the OA. This has proven that the spectral separability between classes does affect the accuracy of benthic habitat classification and there is a significant relationship between spectral separability and the accuracy of benthic habitat classification.