
The inverse-imaging problem comprising the fusing of a hyperspectral image, possessing high spectral resolution, with a multispectral image, having high spatial resolution, to yield an image with high resolution both spatially and spectrally is considered. In particular, a prior state-of-the-art approach—low-rank tensor approximation (LRTA)—is revisited with the goal of simplifying its implementation and accelerating its execution speed. Whereas the original LRTA incorporated low-rank objectives both spatially and spectrally, the revised algorithm employs spectral low-rankness exclusively. Additionally, the reliance of LRTA on singular value thresholding (SVT)—an operator widely used to impose low-rankness in optimizations—is replaced with a fixed-basis approximation that eliminates the computationally costly singular value decomposition required by the SVT. The proposed modifications ultimately result in significant runtime speedup; furthermore, empirical results reveal improved fusion quality when compared to the original LRTA.

Measuring vehicle locations relative to a driver's vehicle is a critical component in the analysis of driving data from both postanalysis (such as in naturalistic driving studies) or in autonomous vehicle navigation. In this work we describe a method to estimate vehicle positions from a forward-looking video camera using intrinsic camera calibration, estimates of extrinsic parameters, and a convolutional neural network trained to detect and locate vehicles in video data. We compare the measurements we achieve with this method with ground truth and with radar data available from a naturalistic driving study. We identify regions where video is preferred, where radar is preferred, and explore trade-offs between the two methods in regions where the preference is more ambiguous. We describe applications of these measurements for transportation analysis.