General

HxGN Content Program’s Orthorectified Imagery

Orthorectified imagery is processed using calibrated sensor parameters and surface elevations to remove optical distortions, sensor perspective, and differential scaling inherent in aerial image acquisition. Orthorectified images are mosaicked into a single image (i.e., Orthomosaic) and then subset into equal area tiles in the final steps before delivery.

Orthomosaic images are processed to maximize resolution and clarity. Automated seamlines are manually adjusted to optimize scene content. All imagery is ground-controlled and subjected to a rigorous QA/QC routine prior to release. Final products yield accurate distance and angular measurements to be used as stable base maps and consistent and reliable image sources for artificial intelligence and machine learning algorithms. A complete Leica-Hexagon workflow from acquisition through processing ensures a highly consistent and reliable product.

HxGN Content Program’s Digital Surface Models

A digital surface model (DSM) includes point elevations (i.e., x, y, and z) that are representative of all primary reflective surfaces of built-up (e.g., buildings, transportation infrastructure) and natural features (e.g., vegetated ground cover). When taken together, the points represent the real world and can be further classified based on material type, vegetation, elevation class or buildings. The HxGN Content Program provides two distinct types of DSM. As part of our Countrywide Program, DSMs are produced via stereo photogrammetric reconstruction. In areas collected as part of the Metro HD Program, DSMs are derived from the first return LiDAR data.

HxGN Content Program’s Digital Elevation Models

A digital elevation model (DEM) includes elevations representative of the bare ground topographic surface devoid of trees, buildings and other surface features. The HxGN content program provides two distinct product types. Image-derived DEMs, as part of the Countrywide Program are a hybrid data product that relies upon an automated grid interpolation to minimize surface features in the data. Metro HD Program-derived DEM data is produced using ground data returns as classified in the point cloud data.

HxGN Content Program’s LiDAR Point Cloud

LiDAR is an active imaging technology that directly measures the distance from the sensor to the surface. The data is reconstructed to form a cloud of points that include values in x, y, and z in a ground reference frame with corresponding intensity returns. When imagery is collected concurrently with lidar, the subsequent elevation points are encoded with spectral information (i.e., R-G-B-NIR) to increase the realism of the point cloud. The supplemental spectral information increases the usability of the data by providing a closer match to reality allowing users and analysts to identify features and materials more easily.

The strengths of LiDAR sensing and data include accurate measurements in the z-dimension, the ability to operate in low-level or dark ambient lighting conditions, and penetrating the canopy. First return LiDAR returns are traditionally used to broadly represent the open ground, vegetation, and infrastructure, while ground points are relied upon to accurately represent the terrain surface. The vast array of the multiple returns between first return and ground points can be subjected to automated and manual classification routines to delineate features of interest, material type or elevation ranges.