LPS eATE at ERDAS Labs

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!

Processing GeoEye-1 GeoStereo Imagery in LPS

I mentioned last week that I would outline the workflow steps in LPS for creating terrain, orthophotos, and 3D vector data from GeoEye-1 GeoStereo imagery.

GeoEye-1 has both panchromatic (0.41 meter resolution) and multispectral (1.65 meter resolution) sensors, and I performed the processing with the panchromatic data because it is higher resolution. For this particular project I have RPC imagery (a single stereo pair) along with surveyed ground control points. Although the imagery is lower resolution than most airborne photography, one of the big benefits is that the image footprint is much bigger - this can dramatically decrease time and effort for certain parts of the workflow (e.g. mosaic seam editing).

In LPS the processing is straightforward: set up the project > measure ground control points > run automatic point measurement > perform the bundle adjustment > generate and edit terrain > perform orthorectification. 3D feature extraction can come anytime after the bundle adjustment is performed. What follows is an overview of the workflow. I won't go into the minute details but will cover the major workflow steps.

Block (project) setup is easy: just choose the model (GeoEye RPC) and then add the images in the LPS Project Manager. At this point processes like image pyramid generation can be performed as well.

A GeoEye-1 project in the LPS Project Manager

After the block is setup, the next step is to use point measurement in either stereo or mono mode to measure the ground control points. This is where we relate file/pixel coordinates with real-world XYZ coordinates from the GCPs. Typically this can be a time-consuming step but LPS has an "automatic XY drive" capability that puts you in the approximate area when you're ready to measure a point. After the GCPs are measured automatic tie point measurement can be run, which will generate tie points. Users have full control over the tie point pattern so there is a high degree of flexibility. After generating tie points, bundle adjustment can be performed in LPS Core. I won't delve into too much detail here, but this process involves running an adjustment, reviewing the results, refining if necessary, and then accepting the results once they are suitable. This is a critical step, because after triangulation we have our first data product: a stereo pair. Stereo pairs are crucial for 3D product generation, because XYZ measurements can be made from them. That means 3D terrain products and vector layers can now be generated.

Viewing a control point in stereo

The next step is to generate a terrain layer that can be used as a source during orthorectification, and may also be used as a product in it's own right. The Automatic Terrain Extraction tool in LPS can generate a surface and allow a high degree of control with regards to post spacing, filtering, smoothing, and more. In this example I used it to generate a 5 meter IMG grid, displayed below.

Terrain displayed in ERDAS TITAN

After performing terrain editing to create a bare earth DEM, I created an orthophoto from one of the 0.5 meter panchromatic GeoEye images. Terrain editing (in stereo) is important because surface objects such as multi-story buildings can introduce error into orthophotos if they are included in the terrain source. The orthophoto is displayed below.

Digital orthophoto in ERDAS TITAN

With a 0.5 meter resolution, it is also possible to extract 3D features such as buildings. I collected a few in PRO600 and then exported them to KML for display in GoogleEarth.

3D KML Buildings in GE

Note that other tools (in this case Stereo Analyst for ERDAS IMAGINE) can also be used for 3D feature extraction. Here is a building collected as a 3D shapefile with texture applied. Note that this is not generic texture, but rather the actual texture from the pan GeoEye-1 imagery. While it doesn't cover all facades, it does add a level of realism that adds to a 3D scene.

In summary, the process for created value-added geospatial data products from GeoEye-1 GeoStereo imagery can be accomplished by following the steps identified above. In a relatively short period of time, an array of 3D products can be derived that have value in a number of different applications. It will be exciting to see what kinds of applications come out of GeoEye-1 stereo imagery in future months and years!

ERDAS Image Web Server Demo Site

With the days of authoring geospatial image products and then shipping them off to customers on DVDs coming to a close, I would like to highlight a demo website for the ERDAS Image Web Server. IWS is a solution for serving imagery to web clients, geospatial applications, or mobile devices. Here's what a snippet of the "Home" section of the site looks like:


The interesting parts of the demo site are in the "IWS in Action" drop down menu. The options provide examples of what is possible with IWS. Some examples include:

• Geo-linking Images
• Geoprocessing
• GIS Integration
• Image Enhancements
• Massive Terabyte-Size Imagery
• Real-Time Tracking
• Reprojection
• Time Series Data
• World Image Gallery
• and samples packaged with IWS

If you're been checking out the new capability for viewing historical imagery in Google Earth 5.0, then check out the "Time Series Data" example - this demonstrates how your own historical or time-series imagery could be served up. Personally I think this is a big potential growth area, as it allows viewers to quickly assess change over time in a specific area of interest. There is a massive amount of historical ortho imagery out there, so it is great to see organizations starting to put together applications for enabling visual change detection.

The World Image Gallery also has a large amount of example datasets. This is worth a look because it displays the performance of datasets of various type and size. Here's a screen shot of a San Diego area mosaic:


The Geoprocessing section shows off some interesting options as well. The example below shows the hillshading capability.

Hopefully you find the site useful, and please feel free to send us feedback or suggestions.

Terrain From Imagery

In 2009 the need for accurate high resolution terrain data is only growing. One can see increasing numbers of applications that use terrain all over the geospatial industry and beyond. Part of the reason for the proliferation of terrain and applications that use it stems from increased usage in all methods of collecting and creating terrain. The SRTM mission provided course-resolution terrain for large portions of the world. LIDAR sensors continue to sell briskly, and of course automatically-generated terrain from triangulated oriented images (stereo pairs) continues to grow as increasing numbers of sensors capture image data.

Automatic terrain generation, which involves producing TINs and grids by correlating XYZ points from a pair of overlapping oriented images, is nothing new. In the context of softcopy photogrammetry the technology is over a decade old, and at ERDAS we have produced automatic terrain extraction solutions since the release of OrthoBase Pro back in 2001. Since then most photogrammetry practitioners have worked automatic terrain generation into their production workflows, particularly as a source for orthophoto generation. While I believe sensor fusion (capturing optical and LIDAR data simultaneously) will have an increasing role in the future, the reality is that automatically-generated terrain is also likely to play an important role for years to come. Why? Because data is becoming captured at increasingly high resolution and software solutions are evolving in their ability to generate corresponding high-density terrain data. A good example is the ADS80 sensor from Leica Geosystems. It can capture imagery at a 5 centimeter pixel resolution. Data captured at this resolution allows for very dense (and accurate) automatically extracted terrain.

In the photogrammetry group at ERDAS we’ve been putting effort into new techniques for automatic terrain extraction from imagery. Specifically we are looking at automating as much as possible, updating terrain extraction algorithms, and supporting modern IT infrastructures by adding distributed processing capability. After all, generating massive terrain datasets can require some serious computer power.

The image below shows terrain with a 10 centimeter density (post spacing) processed as an LAS file and displayed in GeoCue's PointVue LE viewing software (designed for visualizing LIDAR data). Color is based on elevation, so you can easily see the buildings and other surface features. Using a LIDAR viewer as a QC tool helps because of the density of the data.

In the image below I've rotated the view and applied intensity shading. This displays the detail quite well: it is possible easily discern the buildings, roads, crops and vegetation.

And a view of another area:

ERDAS on Twitter

If you want to follow ERDAS news and updates on Twitter, feel free to check us out at www.twitter.com/erdas.

ERDAS Photogrammetry Movies

How to Access and Download LPS 9.3 and other ERDAS Software

I've received a lot of questions from people asking about how they can access and download the new LPS 9.3 release. Since we've changed the methodology for software access, I've outlined the new procedure below. This example covers the download process for LPS 9.3, but it essentially the same for all the products on our new web-site.

The first step is to go to the Product page of the software you would like to download. For LPS, this is here. Note that all the products can be accessed by the product page, and are organized into general functions: Author, Manage, Connect, and Deliver. Clicking on a cube (e.g. Author) at the top will filter the product list.
Next, click the downloads tab on the right-hand side of the tab list.

After clicking the download tab, the content will change and you'll see a download link. After clicking on this you will be directed to log in. Please note that this log in only pertains to software download access, and you will have to register with some basic details.

After registering, an email is sent with your login details. After you login, you can go to the product download tab again and click on the download link once again. For LPS you will see a message saying that due to the large file size (~600MB for LPS) a temporary ftp login has been created for you and that you will be emailed the ftp location and login details. The email is sent immediately and you should receive it within seconds of hitting the download link.

Next, log on the the FTP site and download the software. Without a license LPS will work in "demo mode" for 30 days, but you can also contact your local representative for an evaluation license (or any questions). You may also contact your local representative for the software package on DVD.

ERDAS and Oracle: Building Geospatial Business Systems

Last Wednesday I had the chance to travel up to Brussels to sit in on our ERDAS & Oracle technology seminar: "Building Geospatial Business Systems". It was a full-house and the day was full of both ERDAS and Oracle product information, as well as customer presentations of business solutions they have created by synthesizing technology from both companies.

Some of the focus areas included:
- A introduction and discussion on ERDAS APOLLO, which we just announced last week.
- The ERDAS commitment to OGC standards, including the importance and priority of interoperability.
- Oracle Spatial in Europe, presented by EMEA Oracle Spatial Product Manager Mike Turnill.
- A discussion on the ERDAS product roadmap and direction.

Here are a few snaps from my camera phone. Overall it was a great event: full turnout, great discussions, and a high level of interaction and information exchange.

Now Released: LPS 9.3

We are pleased to announce the release of LPS 9.3 today! This is a major milestone for ERDAS, as we're launching new releases across all product lines today, which is a first. Additionally, we've completely updated the website at www.erdas.com. Each product can be downloaded from the site from the various product sections via the "Downloads" tab. For LPS 9.3, go here and click on the Downloads tab. Note that you'll need to register to access the download. You can also get a new 9.3 license from the link on the www.erdas.com front page.


The main theme of the LPS 9.3 release is 3D feature extraction, with the introduction of "PRO600 Fundamentals for PowerMap XM": PRO600 Fundamentals is a streamlined stereo feature extraction software. Basically we've made PRO600's PROCART module (3D feature extraction) available for Bentley PowerMap XM, which is a GIS-oriented application for map production in a 2D or 3D environment. PRO600 Fundamentals also includes LPS Stereo.

I wrote about a couple of the new benefits in previous posts, but I've also included the entire list of improvements below.

In LPS Core, the following improvements have been added:

• Export to KML: This new LPS 9.3 feature exports an LPS block file or group of block files to the KML (keyhole markup language) file format. This feature allows for the export of both image footprints as well as point measurements associated with the block file.
• Improved Automatic Point Measurement (APM) point correlation quality in cases with less than 50% overlap, variable flying height, and in sidelap areas.
• Added support for NITF NCDRD format in the orbital pushbroom QuickBird/WorldView model.
• The Triangulation Point Review user interface has been extended to support Satellite Sensor Models.
• New Support for Image Chipping for NCDRD Sensor Model.
• Registration free .NET and COM: New Registry Free LPS allows users to install different versions of LPS on the same machine.
• Synchronized units of measure for the Average Flying Height (Frame Camera) and Average Elevation (Orbital Pushbroom) defined in the Block Property Setup with the units reported in the block file.
• The Average Elevation, Minimum Elevation and Maximum Elevation units in RPC Model projects are now displayed in the project vertical units in the Frame Editor.
• Synchronized units of measure for GCPs and residuals in the Refinement Report.
• Enhanced Importer for ISAT projects with multiple flight lines.
• Support for EMSEN Hand Wheels.
• Added the LHN95 Geoid model (Switzerland).
• Added Latvian Coordinate System (LKS-92) support, which includes the Latvian Gravimetric Geoid (LGG98).

LPS Automatic Terrain Extraction (ATE)

• DEM Accuracy: Added an option to enter a tolerance in the vertical units of the terrain source to set the accuracy range for the predicted surface value of the area. The Min and Max Z Search Range will change with respect to the accuracy value entered. Providing a reliable tolerance will result better matching quality.
• Added support for all currently supported sensors in Adaptive ATE (not just frame cameras and ADS sensors).
• Reliability has been improved with better memory handling.

LPS Terrain Editor

• Drive to Control Point: In 9.3 a new panel in Terrain Editor enables the display of GCPs and tie points associated with the currently loaded block file. An additional new dialog called “Control Point Display Settings” allows users to filter points in the cell array and choose the rendering settings for the Ground Control Points panel. The user can load some or all of the image pairs that a GCP is projected into. This new tool lets users check the quality of the DTM with respect to GCP, check points and the tie points. This tool can also be used for visual inspection of triangulation results after a bundle block adjustment.
• Post Editor hotkeys: allow a user to quickly move through points by using keyboard arrow keys and adjusting the Z value for selected points in gridded terrain files.
• Enhanced jpeg image display.

ERDAS MosaicPro

• Save to Script Functionality: With the release of LPS 9.2, users were able to batch script the entire MosaicPro process and then execute the script from an MSDOS prompt. In 9.3 it is possible to generate the batch script automatically from the MosaicPro user interface. The script generated from MosaicPro may also be used as a template which can be easily modified. This new feature builds a script file from a combination of the currently open MosaicPro project and/or from previously saved settings from image dodging, color balancing, seam polygons, and exclusion areas. The MosaicPro process can then be run in time-set, batch mode from the MSDOS prompt.
• Improved performance for seam polygon generation with "most nadir", "geometry", and "weighted" options.
• Various reliability improvements.

STEREO ANALYST for ERDAS IMAGINE

• Extend Features to Ground: this new feature uses a 3D Polygon Shapefile and extends the segments of each polygon (as faces) to the ground to form solid features (e.g. Buildings).

PRO600

• Ability in PRODTM for the user to specify the extent within which to load terrain data. This allows very large terrain datasets to be used in PRODTM, in a piece-wise manner.

ORIMA

• For triangulation projects using AD40 data, multiple ADS40 flown at the same time are now supported. This required the change of some file formats. This new approach leads to shorter project creation times.

• CAP-A Release 8.10: New Handling of Orientation Data for ADS40. This new data handling has two primary advantages:

o The amount of disk space to store the project is drastically reduced.
o The startup time of CAP-A is much faster as there is no need to read the *.ori files and find the corresponding orientation for each point.

Defense Productivity Module (DPM)

• Users in classified environments can now process NGA MC&G imagery in LPS photogrammetric workflows if the DPM is installed. This support includes access to AMSD ground and imagery points.
• A new Image Slicer has been created to facilitate cutting of the original imagery into smaller segments for extraction. After slicing, an RPC model may be generated to provide support in ERDAS products without a local DPM license. If an NITF module is licensed, the RPC segments may be exported to NITF with RPC00B tags for interoperability with a wide variety of software packages.

ERDAS UK GeoImaging User Group Conference 2008

If you're in the UK you may be interested in checking out the GeoImaging User Group Conference 2008 hosted by InfoTerra. The event is in Oxford on September 29th and 30th and will cover the entire range of ERDAS desktop and enterprise products.

I will be there on the 29th to deliver a presentation on our photogrammetry product line. The main focus will be on the upcoming LPS 9.3 release, but I'll also cover productivity tips, photogrammetric workflows, and highlight the directions we're going in. Please feel free to sign up and attend if you are in the region!

Short Hiatus

I'll be traveling (and generally away from computers) for the next five weeks so the lights will be out at The Fiducial Mark for the next little while. I'll be back in action by late July and will be reporting from a different location: see below for details!

In the meantime, check out e-planet for the latest posts from some other folks at ERDAS.

Aggregated ERDAS Posts

As you may have noticed from the links on the right, there are several ERDAS-oriented blogs out there. I've been experimenting with Yahoo Pipes - which has impressive functionality - allowing you to combine and manipulate multiple feeds. I embedded the feed results in a web-page here, so please feel free to check it out. It is also located in the links section on the right: e-planet.

Signup for the IMAGINE Objective Beta Program

In case you missed the press release yesterday, the beta release of IMAGINE Objective has been announced. IMAGINE Objective is a tool for multi-scale image classification and feature extraction. I haven't had a chance to directly use the tool, but I've seen several demonstrations and the technology is very promising - I'd definitely encourage people to try it out. I think it can be particularly useful for update mapping applications (e.g. when an organization gets new orthos and needs to update buildings in a newly constructed subdivision that doesn't appear on their previous ortho coverage).

The beta sign-up page is here.

For more detailed info on IMAGINE Objective, check out the feature extraction solution paper. One thing to note: at this point the tool is focused on 2D feature extraction: it does not automatically extract 3D features. Unfortunately nobody has cracked the nut on automatic photogrammetric compilation! For now the 3D tools remain either manual or semi-automatic.

New ERDAS YouTube Channel

We have just setup an ERDAS YouTube channel at: http://www.youtube.com/user/ERDASINC

This is a good way for us to walk through new features and workflows. Instead of learning about a feature as a bullet point on an email or brochure, you can see it live in action. Right now there are a couple of TITAN videos there, and we will be adding more. Feel free to check them out, and I'll post when I put any photogrammetry/mapping videos up!

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:

ASPRS Update and News

It has been a good week so far at this year's ASPRS conference in Portland. This was the first major show we've been to since the ERDAS name change, so we had a completely updated exhibition booth. Here's a snap of it:



The UGM on Tuesday was a good session as well. I spoke about a rapid response mapping project (basically a sensor fusion to data product generation exercise, which I will write about later) and also gave a short demonstration of LPS. There were some good discussions, particularly around the presentation/demo of the IMAGINE Objective feature extraction and classification tool.

In other news, Microsoft Photogrammetry (Vexcel) announced a new software system (called UltraMap) for processing UltraCamX imagery during today's "Photogrammetry Solutions" session. Michael Gruber gave an overview of the new system, which mainly covers the "upstream" part of the photogrammetric workflow (download, pyramid generation, QC, point measurement, etc). A couple interesting points about it that they support distributed processing for some functions (using internal Microsoft technology) and they are using Microsoft's Seadragon technology for image viewing. This is the same technology behind Photosynth, which is impressive.

Heading to Portland, Whereyougonnabe?

Later today I'll be traveling to Portland for the annual ASPRS Conference. The theme of the conference, "Bridging the Horizons - New Frontiers in Geospatial Collaboration", got me thinking about the new (beta) Facebook application called Whereyougonnabe? from Peter Batty and co at Spatial Networking. Whereyougonnabe is a service that allows you to enter in the "where and when" of upcoming trips and activities. The application then lets you know which of your friends will be close to you, making it handy for all kinds of reasons. This sheds some light on the company name: spatial instead of social in "Spatial Networking". I had a chance to play around with it a bit recently and was particularly impressed with the Google Earth support. The application allows you to click on a link that generates a KML file that you can use to fire up Google Earth (and other KML-supporting apps) and view your (and your friend's) trip pathes. See the screenshot below for my trip to Portland:


Not only is your path displayed, but it also has a timeline control. It also generates an icon out of your profile picture, which is useful as well.

Back to ASPRS: I will write some updates throughout the week, but if you happen to be going think about stopping by our UGM tomorrow morning, or come by the booth anytime! We'll be giving out some new ERDAS t-shirts and will also be have a draw for an Iphone between 3 and 4pm on Wednesday.

ERDAS: Back to the Future (Revisited)

As you may have read in the media or my previous post, Leica Geosystems Geospatial Imaging has been rebranded as ERDAS. Check out the new marketing campaign:

Kidding! This is an old ERDAS ad from a 1986 edition of PE&RS. Some prime marketing material: "High Resolution 512 x 512 x32 bit true color display", "9-Track Tapes Handling", and more...

Speaking of PE&RS, I'll have some information on the ASPRS conference in Portland tomorrow. Stay tuned...

Article Update: Photogrammetry Workflows, Present and Future

In this post I thought I would update an article I wrote last year that provides an intro to photogrammetric workflows and some thoughts on the latest technology. Originally published last May in GIS Development, this version has updated content and I've also added in links to further information on the various topics discussed throughout the article.

Hope you enjoy!

Introduction

The photogrammetric workflow has been relatively static since the advent of digital photogrammetry. Numerous application tools are dedicated to various parts of the workflow but the actual photogrammetric tasks have seen little change in recent years. However, we are beginning to see changes in the workflows. The growing proliferation of “new” technologies such a LIDAR, pushbroom, and satellite sensors has caused many commercial vendors to re-examine the application tools they offer. In addition, advances in information technology have opened up the possibility to processing increasingly large quantities of data. This, coupled with improved processing capabilities and network bandwidth, are also causing a change in traditional photogrammetric workflows.

Background

ERDAS has a long history in providing both analytical and digital photogrammetry solutions. As a Hexagon company, ERDAS’ mapping legacy dates back to the 1920’s with the founding of Kern Aarau and Wild Heerbrugg. These companies were consolidated into Leica and over the years offered analogue, analytical, and digital photogrammetry and mapping solutions. LH Systems, ERDAS, and Azimuth Corp. were acquired by Leica Geosystems in 2001. These acquisitions allowed Leica to enter a number of spaces in the digital photogrammetry market and offer comprehensive photogrammetric solutions to the production photogrammetry, defense, and GIS markets.

ERDAS’ initial photogrammetric offerings, Orthobase and Stereo Analyst for IMAGINE, were targeted at the GIS user community. As demand for 3D data grew in the GIS community, Leica Geosystems sought to provide easy to use tools for producing “oriented” images from airborne or satellite data and extracting 3D information such as building and road data. With the acquisition of LH Systems in 2001, Leica Geosystems inherited a staff and customer base skilled in production photogrammetry. This new customer base required engineering-level accuracy and primarily worked with large-scale airborne photography in the commercial arena and satellite imagery in the defense market. In early 2004 Leica Geosystems released the Leica Photogrammetry Suite (now LPS). This new product suite initially used updated components from OrthoPase and OrthoBase Pro, and developed new technology for stereo viewing and terrain editing. Shortly thereafter mature products such as PRO600 and ORIMA were integrated into the product suite and numerous update releases increased productivity. In April 2008, Leica Geosystems Geospatial Imaging division was re-branded as ERDAS.

Current Workflows

When asked about the “photogrammetric workflow” most industry professionals will refer to the analog frame camera (e.g. RC30) workflow. Analog frame cameras were prevalent during the transition to digital photogrammetry and still remain a common source of imagery. Numerous software tools have been developed to guide users through the traditional analog frame workflow. Popular vendors include BAE, INPHO (now owned by Trimble), Intergraph, and ERDAS. A brief outline of the mainstream analog frame workflow is provided below.

· Scanning process: Airborne camera film is scanned and converted into a digital file format. Some high performance scanners perform interior orientation (IO) as well.

· Image Dodging: Scanning may introduce radiometric problems such as hotspots (bright areas) and vignetting (dark corners). These can be minimized or reduced by applying a dodging algorithm. Dodging, in the digital photogrammetry sense of the word, generally calculates a set of input statistics describing the radiometry of a group of images. Then, based on user preferences, it generates target output values for every input pixel. Output image pixels are then shifted based on several user parameters and constraints from their current DN value to their target DN. Typically there are options for global statistics calculations for a group of images, which has the net effect of balancing out large radiometric differences between images. Overall this has the effect of resolving the aforementioned problems and “evening out” the radiometry both within individual images and across groups of imagery.

· Project setup: most photogrammetric packages have an initial step where the operator performs steps such as defining a coordinate system for the project, adding images to the project, and providing the photogrammetric system with general information regarding the project. Ancillary information may include data such as flying height, sensor type, the rotation system, and photo direction.

· Camera Information: the operator needs to provide information about the type of camera used in the project. Typically the camera information is stored in an external “camera file” and may be used many times after it is initially defined. It contains information such as focal length, principal point offset, fiducial mark information, and radial lens distortion. Camera file information is typically gathered from the camera calibration report associated with a specific camera.

· Interior Orientation (IO): The interior orientation process relates film coordinates to the image pixel coordinate system of the scanned image. IO can often be performed as an automatic process if it was not performed during the scanning process.

· Aerial Triangulation (AT): The AT process serves to orient images in the project to both one another and a ground coordinate system. The goal is to solve the orientation parameters (X, Y, Z, omega, phi, kappa) for each image. True ground coordinates for each measured point will also be established. The AT process can be the most time-consuming and critical component of the digital photogrammetry workflow. Sub-components of the AT process include:

o Measuring ground control points (typically surveyed points).

o Establishing an initial approximation of the orientation parameters (rough orientation).

o Measuring tie points. This is often an automatic procedure in digital photogrammetry systems.

o Performing the bundle adjustment.

o Refining the solution: this involves removing or re-measuring inaccurate points until the solution is within an acceptable error tolerance. Most commercial software packages contain an error reporting mechanism to assist in refining the solution.

• Terrain Generation: Digital orthophotos are one of the primary end-products in the photogrammetric workflow. Accurate terrain models are an essential ingredient in the generation of digital orthophotos. They are also useful products in their own right, with uses in many vertical market applications (e.g. hydrology modeling, visual simulation applications, line-of-sight studies, etcetera). Terrain models can take the form of TINs (Triangulated Irregular Network) or Grids. Once AT is complete, terrain generation can typically be run as an automatic process in most photogrammetric packages. Automatic terrain generation algorithms typically match “terrain points” on one two or more images (more images increase the reliability of the point). Seed data such as manually extracted vector files, control points, or other data can often be input to help guide the correlation process. There are usually filtering options to remove blunders, also referred to as “spikes” or “wells” in the output terrain model. Filtering can also be used to assist in the removal of surface features such as buildings and trees. This can be of great assistance if the desired output is a “bare-earth” terrain model. It is important to note that terrain may also be acquired via manual compilation (in stereo), LIDAR, IFSAR (Interferometric Synthetic Aperture Radar), or publicly available datasets such as SRTM.
• Terrain Editing: Digital terrain models (DTMs) that have been generated by autocorrelation procedures typically require some “cleanup” activities to model the terrain to the required level of accuracy. Most photogrammetric packages include some capability of editing terrain in stereo. It is important for operators to see the terrain graphics rendered over imagery in stereo so that they can determine if automatically generated terrain posts are indeed “on the ground”. That is, that the DTM is an accurate representation of the terrain, or is at least accurate enough for the specific project at hand. Terrain can usually be rendered using a mesh, contours, points, and breaklines. The operator usually has control over which rendering method is used (it could be a combination) as well as various graphic details such as contour spacing, color, line thickness and more. Terrain editing applications usually provide a number of tools for editing TIN and Grid terrain models. In addition to individual post editing (e.g. add, delete, move for TIN posts, adjust Z for Grid cells), area editing tools can be used for a number of operations. These may include smoothing, surface fitting operations, spike and well removal tools, and so on. Geomorphic tools can be used for editing linear features such as a row of trees or hedges. After a terrain edit has been performed, the system will update the display in the viewer so that the operator can assess the accuracy and validity of the edit. Once the editing process is complete the user may have to convert it into a customer-specified output format (e.g. one TIN format to another, or TIN to Grid). DTMs are increasingly a customer deliverable and product, as mentioned previously they have many uses and are becoming quite widespread in various applications.
• Feature Extraction: Planimetric feature extraction is usually an optional step in the workflow, depending on the project specifications. Automatic 3D feature extraction algorithms are under development, but manual stereo extraction is still the predominant method. Feature extraction tools in digital photogrammetry packages typically allow users to collect, edit and attribute point, line, and polygonal features. Features can be products in themselves, feeding into a 3D GIS or CAD environment. Alternatively building futures may be used again in the photogrammetric processing chain in the production of “true orthos”, which take surface features into account to produce imagery with minimized building lean – which can be particularly beneficial in urban environments.
• Orthophoto Generation and Mosaicing: Digital Orthophotos are usually the primary final product derived from the photogrammetric workflow. There are many different customer specifications for orthos, including accuracy, radiometric quality, GSD, output tile definitions, output projection, output file format and more. A mosaicing process is usually included in the ortho workflow to produce a smooth, seamless, and radiometrically appealing product for the entire project area. Mosaicing may be performed as part of the orthophoto process directly (ortho-mosaicking) or performed as post-process later on. Generally, orthophoto production follows these steps:
• Input image selection: the operator chooses the images to be orthorectificed.
• Terrain source selection: the operator chooses the DTM to be used for orthorectification. This is a critical step, as the accuracy of the orthophoto will be determined by the accuracy of the terrain. A terrain model with gross errors (e.g. a hill not modeled correctly) will result in geometric errors in the resulting orthophoto.
• Define orthophoto options: Operators typical select a number of option for the orthorectification process. These may include output GSD, the image resampling method, projection, output coordinates and more.

Aside from defining the various parameters, the orthorectification process is not usually an interactive process. However, the mosaicing process usually does involve some degree of operator interaction. After images are chosen for the mosaic process, there is usually some method of defining seams (polygons or lines used to determine which areas of the input images will be used in the output mosaic). While there are many automatic seam generation applications, there is almost always some element of user interaction to either define or edit seams – or at least review the seams. Operators will typically edit the seams so that they run along radiometrically contiguous areas. That is, they do not cut through well-defined features such as buildings. This is because the ultimate goal of seam editing is to “hide” the seams so that they are not visible in the output mosaic. Once seams are defined, they can usually have smoothing or feathering operations applied to them so that their appearance is minimized.

Another important aspect is radiometry. While some operators will tackle radiometry early on in the workflow (as previously discussed in the “Image Dodging” step), others will dodge or apply other radiometric algorithms during the orthomosaic production process. The goal is to make the output group of images radiometrically homogeneous. This will result in a visually appealing output mosaic that has consistent radiometric qualities across the group of images comprising the project area.

A project area may be several hundred square kilometers in size, so a single output mosaic file is not usually an option due to the sheer size. End customers cannot usually handle a single large file and would prefer to receive their digital orthomosaic in a series of tiles defined by their specification. Most photogrammetric systems have a method of defining a tiling system that can be ingested by the orthomosaicing application to produce a seamless tiled output product.

In recent years the introduction of high resolution satellite imagery and airborne pushbroom sensors such as the ADS40 have added new variations to the traditional workflow. Both types of sensors product data that are digital from the point of capture, alleviating the need to scan film photography. Commercially available satellite imagery (e.g. CARTOSAT, ALOS, etcetera) has been available at increasingly high levels of resolution (e.g. 80cm resolution for CARTOSAT-2). While this is sufficient for many mapping projects, some engineering level project applications still require the resolution available from airborne sensors.

Pushbroom sensors such as the ADS40 can achieve a ground sample distance in the 5-10cm range. Modern digital airborne sensors are also usually mounted with a GPS/IMU system. GPS (Global Positioning System) technology assists mapping projects by using a series of base stations in the project area and a constellation of satellites providing positional information accessed by the GPS receiver on-board an aircraft. IMU’s (Inertial Measurement Unit) are increasingly used to establish precise orientation angles (pitch, yaw, and roll) for the sensor platform in relation to the ground coordinate system. GPS and IMU information can be extremely beneficial for mapping areas where limited ground control information is available (e.g. rugged terrain). They also assist in the triangulation process by providing highly accurate initial orientation data, which is then further refined by the bundle adjustment procedure. GPS and IMU information can also be used for “direct georeferencing”, which bypasses the time-consuming AT process. However, direct georeferencing is not a universally-accepted methodology within the mapping community. The caveat to direct georeferencing is that project accuracy may suffer – however this may be acceptable for rapid response mapping and other types of projects where lower accuracies are adequate for the end customer.

Thoughts on Current and Future Photogrammetric Workflows

We are beginning to see some shifts in the currents guiding photogrammetric workflows. These shifts are being driving by advances in computing hardware, new sensor technology, and enterprise solutions.

Data storage and dissemination is dynamic area in the industry. While imagery was traditionally backed up on tape systems, the cost of storage has dramatically declined in recent years. As customer demand for high-resolution data increases, it is becoming less practical for users to store data directly on their workstations. Users are increasingly storing imagery on servers, employing different methods for accessing it. Demand appears to be in the increase for tools to manage and archive data. Organizations are also examining the possibility of sharing and publishing data. The data may stored on servers and published via web services or made available for access, subscription, or purchase via a portal.

Sensor hardware is also rapidly changing the photogrammetric workflow. LIDAR has now been widely adopted and accepted, providing extremely high-density and high-accuracy terrain data. In addition to LIDAR, there is a growing trend of integrating LIDAR with digital frame sensors, which enables the simultaneous collection of optical and terrain data, enabling rapid digital orthophoto processing. This is much more cost-effective than flying a project area with multiple sensors for image and terrain data. When coupled with airborne GPS and IMU technology, terrain and georeferenced imagery – the primary ingredients for orthos – can be available shortly after the data is downloaded after a flight. IFSAR mapping systems are also a growing source of terrain data.

Coupled with explosion of imagery is the need to efficiently process it. One method that researchers and software vendors have begun exploring is distributed processing. Under this model a processing job is divided up into portions which are then submitted to remote “processing nodes”, which results in a significant improvement in overall throughput for large projects. Most commercial efforts, such as the ERDAS Ortho Accelerator, have focused on ortho processing. However there also several other photogrammetric tasks that lend themselves to distributed processing solutions (e.g. terrain correlation, point matching, etc.).

With data increasingly stored on network locations and the general adoption of database management systems, enterprise photogrammetric solutions will likely change the face of the classical photogrammetric workflow. With imagery and other geospatial data increasingly stored on servers, the processing framework is likely to change such that the operator interacts with a client application that kicks off photogrammetric and geospatial processing operations. Rather than running a heavy “digital photogrammetry workstation”, or DPW, the operator will be operating a client view into the project. Also, geospatial servers will enable organizations to store and reuse project and other data. For example, automatic correlation processes could automatically identify and utilize seed data stored in online databases, or terrain data stored from previous jobs. The notion of collecting data once and using it many times will be prevalent. Large quantities of data such as airborne and terrestrial LIDAR-derived point clouds will be able to be stored and have operations such as filtering, classification and 3D feature extraction applied to them. With a shift to enterprise solutions, industry adoption of open standards (e.g. Open GIS Consortium) will be critical. Providing open and extensible systems will allows organizations to customize workflows to meet their specific needs, fully enabling their investment in enterprise technology.

Conclusions

This is an exciting time for those of us in the photogrammetry group at ERDAS. Recent trends discussed above have opened up new avenues for changing, modernizing, and empowering what was until recently a relatively static workflow. Our customers drive us to deliver solutions that meet a variety of needs. While there is the constant need to pay attention to existing workflows, it is important to keep an eye on technology trends that will guide future workflow directions. Enterprise integration will likely change the face of classical photogrammetric workflows, making photogrammetry a ubiquitous component of modern geospatial business decision systems.

ERDAS: Back to the Future

The big news today is that Leica Geosystems Geospatial Imaging (LGGI) has been reborn as ERDAS. Check out www.erdas.com for the new look of the web-site. Joe Francica from Directions Magazine was here last week and wrote an in-depth article on the name change and plans moving forward.

So what does this mean for all the Leica-oriented product names??? Well, as you can see from the new web-site, the Leica name has been replaced with ERDAS (e.g. Leica Image Manager becomes ERDAS Image Manager). For Leica Photogrammetry Suite, we decided to just turn it into "LPS". Since our photogrammetry technology is tightly coupled to the sensor world where the Leica brand has a lot of value, it made sense to keep our link to our heritage with LH Systems, Leica Geosystems, and so forth.

Overall, an exciting development - it is great to be able to bring back one of the most recognizable names in the geospatial industry!