Thursday, June 11, 2009

2009 NAIP Status Update

As mentioned on the NSGIC News Blog, the National Agriculture Imagery Program (NAIP) for 2009 will cover roughly two-thirds of the USA. This is quite an achievement for the program and will result in an excellent resource for geospatial practitioners as well as the broader public.

A status update from yesterday indicates that flight operations are already well underway.

The 2009 program contractors include a group of commercial mapping firms that are all well-known in the North American mapping industry: 3001, Aerial Services, the North West Group, Photo Science, Sanborn and Surdex. It is interesting to note that the cameras used will be a mix of large-format frame and pushbroom sensors. 3001 has both a Leica Geosystems ADS40 (pushbroom) as well as an Intergraph DMC (frame), and I'm not sure which will be used for NAIP acquisition. Aerial Services and the North West Group operate Leica ADS sensors. Photo Science and Surdex operate DMCs while Sanborn operates a Microsoft Ultra Cam (frame). The photogrammetric processing workflows for frame and pushbroom sensors are quite different, with pushbroom sensors capturing long strips of imagery in a "pixel carpet" versus the traditional frame approach. However, it is good to see a mix of technology in use.

Here is a map of the contractor areas:

Note that further maps and status updates are available from the APFO (Aerial Photography Field Office) home page.

Wednesday, June 10, 2009

Terrain Point Cloud Extraction from High Resolution Optical Images

If you've been to ERDAS Labs then you probably know that we are developing a new automatic terrain extraction solution. One of the unique things about the project is that it has given us the opportunity to think carefully about how we persist terrain data, and last week I had a chance to discuss this at the ISPRS Workshop in Hannover, Germany. I don't have a recording of the presentation, but here are some of the details:

1) Digital imagery is achieving increasingly high resolutions. We are now at a stage where airborne sensors can achieve higher than 5 centimeter pixel resolution.

5cm Resolution ADS80 Imagery

2) Many softcopy auto-correlation systems (XYZ terrain point matching system) were initially developed upwards of a decade ago, and were not designed to take advantage of high resolution sensors. Our own LPS ATE module was originally released in 2001 as "OrthoBase Pro" (timeline here). One of the features of some more modern systems is the ability to attempt correlation on every pixel - which can yield a very large volume of data.

117 Million Auto-Correlated Terrain Points

Detailed View: Terrain Points on Individual Boats

3) TINs and Grids, the traditional formats for persisting terrain data in softcopy photogrammetry and GIS, may not be optimal for high resolution terrain data: hundreds of millions of points at a high density. Both have pro's and cons, which Gene Roe has outlined here. Grids can be redundant (particularly for flat regions) and while TINs are very flexible in this regard, they have no standard format. Each vendor has their own implementation, making data translation and transportability a challenge - not to mention long-term storage.

4) The LAS format, while designed for use with LIDAR sensors, may be a viable alternative to TINs and Grids for autocorrelated terrain data. Why? There are a few different reasons:
  • LAS is an ASPRS-administered standard and has a high adoption rate among geospatial software vendors.
  • The LAS 1.2 specification supports attribution, for example the ability to encode an RGB value for each terrain point. While it isn't commonly used within the LIDAR community, it is very useful for auto-correlated terrain. This allows RGB-encoded terrain to be used for applications such as visualization. Capabilities such as this are not possible with the traditional TIN/Grid approach.
  • When correlating on every image pixel, terrain data can be very dense. A compelling research area involves applying LIDAR classification and filtering techniques to autocorrelated terrain data.
Point Cloud with Color Attributes (CIR, Red, Green: ADS80 Imagery)

Detailed View: Point Cloud with Color Attributes (CIR, Red, Green: ADS80 Imagery)

The images above show color attribute encoding for an LAS 1.2 point cloud that was processed from stereo ADS80 imagery. The bottom image is a zoomed in perspective view showing a lot of detail: solar panels on the roof, cars, and a feeling of depth in the empty pool. These images show the point cloud rendered as a TIN within the FugroViewer. As you can see, the terrain representation is quite different from a traditional TIN or grid.

Thursday, June 4, 2009


You may have seen the press release regarding the new ERDAS Labs website. One area I would like to highlight is the LPS eATE Preview section. This features a movie and a blog post (New Algorithm for Automatic Terrain Extraction) by Dr. Neil Woodhouse, who has been involved with our new terrain project since the start. As he suggests in the post, we chose to develop a completely new solution rather than incrementally improving the current LPS ATE product - which was originally released as Orthobase Pro back in 2001.

Both the movie and the article feature Neil working with and discussing auto-correlated terrain persisted in the LAS format, which is more commonly associated with LIDAR data but also makes a great deal of sense for dense (high resolution) terrain data as well. Note that the software used to present the data Neil discusses in the movie is the FugroViewer, which I discussed here. One of the nice aspects of the FugroViewer is that is shows RGB-encoding for both the point cloud and a derived TIN - which is great for visualization of the terrain surface.

One other thing to note is that eATE is still under development, but we are looking forward to releasing the initial version!