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Fusing Point-Cloud Data With Imagery

Adam O'Connor

By now we've all seen the power of multi-sensor data fusion to facilitate situational awareness which enhances our ability to understand and interpret a specific environment. Taking the most valuable components of disparate data sources then fusing them together can enrich contextual analysis and help us make better decisions based on the extraction of meaningful information from the fused data. When working with geospatial data such as LiDAR point-clouds and high-resolution imagery a relatively simple yet powerful technique is to utilize the georeferencing spatial reference metadata to encode each 3D point with corresponding image pixel values based on data geopositioning. This enables more realistic 3D visualization of the point-cloud data since the points can be displayed using colors derived from an alternate raster data source.

Fortunately the LAS format specification provides the ability to store RGB color information for every point stored in a *.las file. However, when a LiDAR data collection project is performed it does not always include cotemporal image acquisition so the process of coloring a point-cloud may need to be executed at a later time using raster data from a variety of sensors (e.g. EO/IR, SAR, etc.). For example, some of the Elevation Source Data (3DEP) available for download from The National Map does not include the RGB color information so it can be beneficial to also download corresponding High-Resolution Orthoimagery (HRO) then fuse the two datasets together.

With this in mind we have been working diligently on a new "Color Point Cloud" tool (and corresponding programmatic API task) within the upcoming ENVI 6.0 software version planned for release later this year. The new "Color Point Cloud" tool+task will allow users to process 3D point-cloud data along with any geographically overlapping raster dataset to generate a new output LAS 1.2 format *.las file which is RGB encoded with pixel values from user-selected image bands. This new processing capability also allows the user decide how to handle points that fall outside the spatial extent of the raster imagery by either removing the data from the generated output *.las result or simply coloring them all black (RGB=0,0,0):

Screenshot of ENVI's new "Color Point Cloud" Tool

Consider the USGS LiDAR Point-Cloud (LPC) source data that can be downloaded from The National Map for San Francisco, CA. Since the LAS datasets do not include RGB encoding a 3D point-cloud visualization will typically involve a simple colormap based on height attributes perhaps with shading based on intensity. While specific features are clearly visible in this style of data visualization it can be difficult to visually interpret the point-cloud:

Data downloaded from The National Map courtesy of USGS

Fusing this point-cloud data with the 1-foot resolution imagery also available for this region yields a much more realistic visual representation:

Data downloaded from The National Map courtesy of USGS

Keep in mind there's no rule that says the point-cloud RGB encoding must come Red | Green | Blue image channels which is why ENVI's "Color Point Cloud" tool+task is very flexible and allows the user to select any 3 bands from any raster dataset. For example, users can also utilize infrared bands from multispectral or hyperspectral datasets to obtain more complex coloring of the point-cloud data such as a CIR representation:

Data downloaded from The National Map courtesy of USGS

Moving forward we plan to support other point-cloud storage formats such as BPF (Binary Point File) and SIPC (Sensor Independent Point Cloud) that provide the ability to store even more per-point auxiliary attribute data that will enable not just visualization but also specialized algorithm development for automated analysis of fused 3D data products.


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