Time-Series Imaging of Ocean Waves with an Airborne RGB and NIR Sensor

Measurements from the Airborne Remote Optical Spotlight System (AROSS), an airborne, panchromatic, imaging system, have been used to successfully produce frequency-wavenumber spectra of shoaling ocean waves. The fidelity and quality of the spectra have enabled accurate retrievals of water depths, currents, and surf characteristics and these results have been reported in previous publications. A next-generation system, based on AROSS, has been constructed with 4 CCD cameras to provide simultaneous measurements of 4 color bands, nominally chosen to be red, green, blue and near-infrared. This system, called AROSS multi-channel (AROSS-MC), provides simultaneous spectral time-series imagery, enabling measurements of water clarity, sediment transport, and turbulence characteristics, in addition to the previous products. One of the main obstacles to providing good-quality data from a multi-camera system is the ability to accurately merge imagery from the cameras to a sub-pixel level. A straightforward and reliable laboratory method was developed to ensure significant overlap, to approximately 5 pixels, of the cameras using a diffractive-optical-element. In addition to this laboratory alignment between cameras, an independent software method was developed to use field data to fine-align multiple cameras to sub-pixel accuracy. We show that the imagery can be merged to an accuracy of 0.07 pixels with this automated algorithmic technique. The need for camera calibration is a fundamental requirement in the context of quantitative geodetic data processing. Geometric calibration of the cameras is important for geo-rectifying the imagery to retrieve wave parameter data obtained from the time-series imagery, such as wavelength and speed of the shoaling waves, water depth, and current. In addition to geometric (focal length and distortion) and flat field calibration for each individual camera, multi-camera and multi-spectral systems require additional measurements, such as inter-camera alignment. This paper presents the architecture of the system and the approach for providing simultaneous 4-color imagery in space and time. The algorithm for aligning the bands is documented with statistics describing how well the color images are correlated. Example data products display our success in providing correct spectral-temporal-spatial data. For example, high contrast images of small colored objects are devoid of spectral anomalies