LIDAR and Imagery Collection for Rapid Response Mapping

At our ASPRS UGM last Tuesday I presented a case study on rapid response mapping. The case study was an interesting application, so I thought I would share it here as well. The focus was on a joint ERDAS (software) and Leica Geosystems (hardware) exercise conducted last summer called "Empire Challenge 2007". This was joint military exercise for testing intelligence, surveillance and reconnaissance (ISR) concepts. The exercise was initially developed after technical issues were identified in sharing ISR information between allies in hotspots such as Afghanistan. I wasn't personally at the event, held near China Lake (California), but did get a chance to work with some of the data that was collected.

For the ERDAS/Leica team, the exercise involved flying a Cessna 210 mounted with both LIDAR and optical sensors over a project area and then creating final data products immediately after downloading the data. The team spent approximately three weeks on-site, and during this time they worked in three project areas and were able to fly, collect, and process a few thousand images and a massive quantity of LIDAR data.

The hardware consisted of an ALS50 (LIDAR), the soon-to-be-released RCD105 digital sensor, as well as Airborne GPS/IMU, a GPS Base Station, and some data processing workstations. While not officially released by Leica, the RCD105 was first "announced" at last years Photogrammetry Week in Stuttgart, Germany. More specifically it was discussed in this paper by Doug Flint and Juergen Dold. It is a 39 megapixel medium-format digital camera - which makes for a great solution when coupled with the ALS50 airborne LIDAR system. Here is an image of the RCD105:The software mix covered several areas. These included:

• Flight planning and collection software (FPES and FCMS)
• LIDAR processing (TerraScan)
• GPS and IMU processing (IPAS Pro, IPAS, CO, and GrafNav)
• Image processing (ERDAS IMAGINE)
• Photogrammetric Processing (LPS, ERDAS MosaicPro, ERDAS ImageEqualizer)
• Quality Control (ERDAS IMAGINE)
  

There were three main missions that were flown. These included:

• A basemap collection flight. At 3048 meters, this was the highest altitude flight. The imagery GSD (Ground Sample Distance) was 0.3 meters. Data products included an orthomosaic, georeferenced NITF (National Imagery Transmission Format) stereo pairs, NITF orthos, a LIDAR point cloud, and a LIDAR DEM.
• A tactical mapping mission. This was a lower altitude flight (914 meters) collecting imagery at a GSD of 0.06. This was "tactical" as it covered specific project areas - as opposed to the broad swath of data collected from the higher altitude basemapping flight. Data products included an orthomosaic, geoferenced NITF steree pairs, NITF orthos, and a LIDAR point cloud and DEM.
• An IED corridor mission: basically covering a linear feature (a road). This was the lowest altitude and highest resolution flight (at 305 meters and 0.04 GSD), which produced NITF stereo pairs, NITF orthos, as well as a LIDAR point cloud and DEM.

The project area was pretty typical inland Southern California scrub/desert. Here's a photo from the ground:

And here's one of the images (in this case shown during point measurement - a part of the triangulation process - in LPS):

As you can see, the radiometry is tough! This is why ImageEqualizer had to be used to perform radiometric corrections.

Most of the workflow was relatively standard (mission planning, data collection, and the photogrammetric processing), but some of the final product preparation steps were pretty interesting. Since one of the main goals of the entire exercise was to produce intelligence products that could be shared with other groups, special consideration had to be given to exactly how the data would be formatted for delivery to the other Empire Challenge groups ingesting the data. Since the groups accepting the data could have been using any number of software packages, the ERDAS/Leica team had to steer clear of proprietary formats. However, one thing that many image processing and photogrammetry products usually have in common is the ability to ingest images with an associated RPC (Rational Polynomial Coefficient) model. Here is a good description of RPCs in GeoTIFF. Since this was a military exercise, the images (processed as tiffs) had RPCs generated in IMAGINE and then were exported to NITF. This made is possible to pass along the final data products to several groups without any data format/interoperability issues. One thing to note is that "RPC Generation" was introduced in the IMAGINE 9.1 release in early 2007.

By the end of the project the total processing times for the various missions could be measured in hours. The basemap mission took the longest (about three days for the entire end-to-end process), but it had approximately 900 images along with the LIDAR data.

Here's a screenshot of an orthomosaic over terrain. The radiometry hasn't been fully processed in this image, but it gives you an idea of what the project area was like: