A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

Remote sensing is useful for detecting and quantifying cyanobacteria blooms for managing water systems. In particular, airborne hyperspectral remote sensing has an advantage in precise cyanobacteria detection with high spatial and spectral resolution. Many bio-optical algorithms have been developed and utilized to estimate algal concentration. However, achieving the optimal conventional optical model accuracy is still challenging in freshwater owing to the biophysical complexity of the inland water and the seasonal reflection of site-specific optical properties. Thus, this study applied convolutional neural network (CNN) with various input windows to estimate the concentrations of phycocyanin (PC) and chlorophyll-a (Chl-a), and generated a phytoplankton pigment map. We proposed that the Point-centered regression CNN (PRCNN) showed accurate PC and Chl-a simulations, with R2 > 0.86 and 0.73, respectively, and root mean square errors of