- Usama Mahmood, Computer Science, University of Delaware
- Gonzalo Arce, Electrical and Computer Engineering, University of Delaware
LiDAR remote sensing systems are utilized in diverse platforms, including satellites, airplanes, and drones, each influencing the sampling characteristics of the underlying imaging system. Low-altitude LiDARs offer high photon count and spatial resolution but are limited to localized patches. On the other hand, satellite LiDARs provide global-scale measurements but suffer from sparse samples along swath line trajectories. Leveraging hyper-spectral data with the same spatial resolution, we introduce the concept of HyperHeight Data Cubes (HHDC), a novel representation of waveform altimetry profiles that encapsulate rich information about the scene’s 3D structure. HHDC allows for easy extraction of canopy height models, digital terrain models, and other scene features using simple statistical quantiles. The dataset for this study is obtained from NEON. Simulations conducted with various types of forests across the US demonstrate the remarkable capabilities of the new LiDAR imaging systems empowered by the integration of hyper-spectral data. In the future, this approach aims to apply the principles of compressive sensing and machine learning (ML) to compressively sense Earth from hundreds of kilometers above its surface and then reconstruct 3D imagery with high resolution and coverage, as if the data were collected from airborne platforms at just hundreds of meters in height. We can use Machine learning methods to reconstruct the compressive LiDAR measurements, enabling high-resolution, dense coverage, and broad field-of-view per swath pass.