Airborne Lidar Bathymetry (ALB) waveforms provide a time log for the interaction of the laser pulse with the environment (water surface, water column and seafloor) along its ray-path geometry. Using the water surface return and the bottom return, it is possible to calculate the water depth. In addition to bathymetry, the ALB bottom return can provide information on seafloor characteristics. The main environmental factors that contribute to the ALB bottom return measurements are: slope, roughness, vegetation, and mineral composition of the surface geology. Both the environment and the ALB hardware affect the bottom return and contribute to the measurement uncertainties. In this study, the ALB bottom return waveform was investigated spatially (i.e., area contributing to the return) and temporally (i.e. the shape of the waveform return) for seafloor characterization. A system-agnostic approach was developed in order to distinguish between the spatial variations of different bottom characteristics. An empirical comparison of bottom characteristics was conducted near the Merrimack River Embayment, Gulf of Maine, USA. The study results showed a good correlation to acoustic backscatter collected over the same area.
Image-based modeling and rendering is currently one of the most challenging topics in Computer Vision and Photogrammetry. The key issue here is building a set of dense correspondence points between two images, namely dense matching or stereo matching. Among all dense matching algorithms, Semi-Global Matching (SGM) is arguably one of the most promising algorithms for real-time stereo vision. Compared with global matching algorithms, SGM aggregates matching cost from several (eight or sixteen) directions rather than only the epipolar line using Dynamic Programming (DP). Thus, SGM eliminates the classical “streaking problem” and greatly improves its accuracy and efficiency. In this paper, we aim at further improvement of SGM accuracy without increasing the computational cost. We propose setting the penalty parameters adaptively according to image edges extracted by edge detectors. We have carried out experiments on the standard Middlebury stereo dataset and evaluated the performance of our modified method with the ground truth. The results have shown a noticeable accuracy improvement compared with the results using fixed penalty parameters while the runtime computational cost was not increased.