Simultaneous invariant normalization of waveform features from bathymetric lidar, SINWav: A Saipan case study

Abstract

Over the past two decades, a major advance that enabled airborne bathymetric lidar to benefit a much wider range of marine science applications was the development of procedures for creating seafloor reflectance mosaics from recorded intensity data. It was recognized that intensity data, derived from the amplitudes of laser returns from the seafloor, contained information related to seafloor albedo and composition. However, the raw intensity data were also found to be related to a number of nuisance parameters, such that, when grided, they exhibited discontinuities, seamlines and other artifacts, hindering their use in benthic habitat mapping. These realizations led to the development of tools and workflows for correcting lidar intensity data to produce seamless seafloor reflectance mosaics. At present, an opportunity exists for another major advance in airborne bathymetric lidar by utilizing not only intensity data, but a large suite of waveform features that describe the shape of the return signal from the seafloor, to characterize benthic habitats and perform ecological assessments. However, similar to raw intensity data, other waveform features exhibit salient discontinuities, seamlines, and other artifacts, if uncorrected. Furthermore, in contrast to the case of intensity data, little work has been done on correction of an entire suite of waveform features to create a set of seamless seafloor mosaics. This study aims to address this need through a novel normalization method that integrates two image blending techniques: Gaussian weighted color matching and Laplacian pyramid blending. The proposed approach, Simultaneous Invariant Normalization of Waveform Features (SINWav), is designed to be invariant to the type of input waveform features, such that feature-specific tuning is unnecessary. To handle vast amounts of data efficiently, we developed a memory-efficient sparse matrix representation. The methods were applied to bathymetric lidar data from Saipan containing 16 different waveform features. Both visual assessments and quantitative analyses using quality metrics indicated that the proposed approach outperforms results derived from raw data and conventional linear transform.