Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

With the general aim of classification and mapping of coral reefs, remote sensing has traditionally been more difficult to implement in comparison with terrestrial equivalents. Images used for the marine environment suffer from environmental limitation (water absorption, scattering, and glint); sensor-related limitations (spectral and spatial resolution); and habitat limitation (substrate spectral similarity). Presented here is an advanced approach for ground-level surveying of a coral reef using a hyperspectral camera (400–1,000 nm) that is able to address all of these limitations. Used from the surface, the image includes a white reference plate that offers a solution for correcting the water column effect. The imaging system produces millimeter size pixels and 80 relevant bands. The data collected have the advantages of both a field point spectrometer (hyperspectral resolution) and a digital camera (spatial resolution). Finally, the availability of pure pixel imagery significantly improves the potential for substrate recognition in comparison with traditionally used remote sensing mixed pixels. In this study, an image of a coral reef table in the Gulf of Aqaba, Red Sea, was classified, demonstrating the benefits of this technology for the first time. Preprocessing includes testing of two normalization approaches, three spectral resolutions, and two spectral ranges. Trained classification was performed using support vector machine that was manually trained and tested against a digital image that provided empirical verification. For the classification of 5 core classes, the best results were achieved using a combination of a 450–660 nm spectral range, 5 nm wide bands, and the employment of red-band normalization. Overall classification accuracy was improved from 86 % for the original image to 99 % for the normalized image. Spectral resolution and spectral ranges seemed to have a limited effect on the classification accuracy. The proposed methodology and the use of automatic classification procedures can be successfully applied for reef survey and monitoring and even upscaled for a large survey.