Photogrammetry News: Photogrammetric Week 2009

It has been a busy summer and as a result I haven't had much time for keeping up to date with The Fiducial Mark. But with an inter-continental move from Belgium back to Canada wrapped up, there is a lot of news in the mapping business to comment on.


One major event that comes along every couple years is Photogrammetry Week in Stuttgart, Germany. This event, which was held a few weeks ago on September 7-11, is a great forum for learning about the latest developments in airborne sensors, software, and general industry trends. For those of us that didn't get a chance to make it over, the Institute for Photogrammetry at the Universität Stuttgart hosts a web-site containing the agenda, photos, and papers from the conference. The "Papers of the 52nd Photogrammetric Week" section contains a gold-mine of information, and a review of the articles provides a look at where things are at in the industry today.

Papers are divided into four sections:

Introduction: Presentation papers from the University of Stuttgart, Hexagon (Leica Geosystems and ERDAS), Intergraph, Vexcel Imaging (Microsoft), Trimble Geospatial, and IGI. These papers provide company overviews, organizations updates and a common focus on sensor updates (e.g. ADS80, UltraCamXp, etc).

Image-based Data Collection: these papers largely focus on airborne camera systems. One interesting paper is "Digital Airborne Camera Performance - the DGPF Test" by Michael Cramer. DGPF is the German Society of Photogrammetry, Remote Sensing, and Geoinformation. The paper discusses an ongoing project evaluating the strengths and weaknesses of various digital sensors, covering systems from Intergraph, Leica Geosystems, Jenaoptronik, Vexcel Imaging, IGI, Rolleimetric, and DLR Munich. The project involved data collection flights over a well-controlled test site near Stuttgart. In reading the paper, it becomes clear how difficult it is to perform precise apples-to-apples tests between systems - given how many factors can impact the performance of a system (e.g. weather). The paper focuses on geometric accuracy and provides detailed information on the studies conducted thus far. It will be interesting when results are available from the radiometry working group, because this is an area where there are a number of differences between the above sensor systems.

Other interesting papers in this section are "Oblique Aerial Photography: a Status Review", and "The Bright Future of High Resolution Satellite Remote Sensing - Will Aerial Photogrammetry Become Obsolete?" The oblique paper is a good reminder of how Pictometry has come to dominate this particular niche. While I don't believe aerial photogrammetry will become obsolete anytime soon, the second paper raises some great points on the development of satellite-based photogrammetry.

LiDAR: Airborne, Terrestrial and Mobile Applications: numerous papers on both hardware and processing developments for airborne and terrestrial LIDAR applications. The intriguing topic here is how mobile laser scanning is becoming increasingly relevant (Gene Roe adds insight on this topic as well here).

Value-Added Photogrammetry: articles providing a look at where current photogrammetric processing research is focused. The topics range from standards (CityGML), sensor to internet workflows (ERDAS is in a unique position of being the only company that can really offer a solution that starts with data capture and ends up with on-line data delivery and web services), digital image matching, cultural heritage, and more. Automated terrain extraction from stereo imagery is being pursued with renewed vigor, and it is good to see standards appear on the radar as well. Although I failed to see any developments on standards with regards to photogrammetric metadata, it will be great progress if CityGML gains momentum for one of photogrammetry's primary data products: 3D models.

Kudos to the conference organizers for sharing the conference materials - it is a valuable resource and greatly beneficial to the broader geospatial community as well. Sensor data and photogrammetric processing technology is the root of 3D geo-information, and it will only be a matter of time before these technologies embed themselves in an even broader array of applications.

A Look at the Open Topography Portal

It was announced in early December, but I just recently came across the Open Topography Portal. The portal has made a large amount of LIDAR data available for active fault areas in both California and Washington. One of the unique aspects of the portal is that it provides web-based tools for processing raw point cloud data prior to download. The download interface is fairly slick, featuring a Google Maps interface allowing you to interactively select an area and then returning the number of points in your selection (guest downloads are limited to under 50 million points). The system displays the bounding coordinates and then allows for the definition of the delivery format.

The portal provides access to standard DEM products, (e.g. filtered bare earth), point clouds, as well as customized DEMs. Here are some of the options for creating a custom download:

Based on the selection area and processing options, the system provides the estimated processing time and then sends an email when the job is complete and ready for download.

I selected a small area and downloaded the point cloud data, which I then imported into ERDAS IMAGINE and created a shaded relief. Here's what it looks like (note that vegetation and buildings are all included, as filtering has not been applied):

Personally I think the user experience of the Open Topography Portal is more intuitive than the broader USGS CLICK (Center for LIDAR Information Coordination and Knowledge) portal. However portals are developing all the time and it is good to see progress in the ease of use and accessibility of advanced processing and download options.

The other notable news regarding the Open Topography Portal concerns the San Diego Supercomputer Center (SDSC) starting cloud computing research - with a special focus on the GEON LIDAR workflow application. This is something to keep an eye on, as LIDAR data is massive and as of yet I haven't heard of any attempts to use cloud-computing for processing or data management - although there have been initiatives in terms of storing LIDAR data in a database (e.g. the folks at LASERDATA use PostGIS). The Open Topography Portal is a collaboration between scientists at the SDSC and earth scientists at Arizona State University.

Photogrammetry at the Acropolis

After a few weeks offline I'm now back and writing from Liege, Belgium. During my time off I had the opportunity to visit the Acropolis in Athens, Greece. While walking up to the Parthenon I noticed there was a terrestrial laser scanner set-up and operational - although unfortunately I didn't get any photos. But that was enough to get me wondering what the project was about. At the top of the Acropolis I found a sign with a short description of the project (photos below). Since it is difficult to read I have reproduced the text below:

DATA ACQUISITION FOR THE PHOTOGRAMMETRIC RECORDING OF THE ACROPOLIS

The Acropolis Restoration Service carries out the project of geometric documentation of the Acropolis hill, the circuit Wall and the Erechtheion, using photogrammetric methods together with 3-dimensional scanning.

All the information to emerge is to be entered in a Geographic Information System (G.I.S) that will be available through the Acropolis Restoration Service's web site (ysma.culture.gr).

Photogrammetry at the Acropolis was also a subject of discussion at the recent ISPRS Conference in Beijing. One of the technical sessions (TS-SS19) was "Recording and Documenting the Acropolis of Athens - From Classical Ancient Greece to Modern Olympics". While I wasn't at the conference, a colleague sent me the paper for "Recording, Modeling, Visualisation and GIS Applications Development for the Acropolis of Athens", by Tsingas et al. The paper discusses the various techniques employed by the project outlined above, which include geodetic field measurements, terrestrial scanning, and photogrammetric data capture and processing. Of the many data products to come out of the project, an interesting one is a top-view orthomosaic with a 10mm resolution. A 22MP camera was used on a balloon system, as motorized vehicles such as helicopters are not permitted to fly above the Acropolis. Also of interest (and news to me) is that Leica Geosystems is a partner in the project. One of the terrestrial scanners is a Leica HD3000, while ERDAS LPS is used for parts of the photogrammetric processing. This included camera calibration, bundle adjustment, and terrain processing.

The paper describes the methodology in detail, and I will see if it is available online anywhere - it provides an excellent discussion of various techniques used in concert to fully capture a highly detailed digital version of the monument. A few other good papers on photogrammetry/mapping at the Acropolis are here and here.

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:

Triangulated Irregular Network (TIN) Formats and Terrain Processing

One issue in the mapping community is that there is no standard format for Triangulated Irregular Network (TIN) terrain files. Most geospatial applications use proprietary formats, which presents serious interchange problems when moving the data around (e.g. from clients to customers, or even within an organization). Unfortunately we are guilty of this as well in LPS, with our LTF TIN format. Many people get around the limitations of proprietary formats by using ASCII as a common interchange format, where the TIN mass points are listed in XYZ for each row (separated by commas, tabs, or whitespace). Some systems also support the notion of points codes for breaklines, which are a critical part of the TIN structure. The main problem with ASCII is that once you get over several million points, the file can be cumbersome to deal with - which means it may be necessary to divide up the data into tiles. Alternatively you can convert the TIN to a raster format, but this can be undesirable if you have dense mass or unevenly distributed points, or if you have breaklines in the TIN (since rasters don't support breaklines).

A case in point is the LIDAR data from the Washington State Geospatial Data Archive. While there are standards for LIDAR data, most commercial applications do not (yet) natively support it. Hence there is a need to make the data available in alternate formats. The data is available in ASCII and TIN format for each quarter quad, but the TIN format is in .e00 format, which is an old interchange format developed by ESRI. Hence, the site recommends Importing the .e00 files into ArcMap in order to use them. The only issue with that is that you need ArcMap... This is likely a reason for including XYZ ASCII as an option as well. Another option is a 3 meter DEM for the entire coverage area, which is possible to download from here. It is in Arc/Info binary grid format though...

So how do we handle all of this in LPS?

There are a few different options here, and the LPS and IMAGINE groups have been working on a solution. Prior to the LPS/IMAGINE 9.2 release, users had to either use the 3D Surface Tool in IMAGINE or the terrain Split and Merge tool in LPS Core. In 9.2, and moving forward in future releases, we are consolidating our efforts by extending the LPS Split and Merge tool and making it available in IMAGINE, which we have renamed to the "Terrain Prep Tool". It is available in Data Prep > Create Surface in IMAGINE, or from Tools > Terrain Prep Tool in the LPS Project Manager.
The Terrain Prep Tool adds new split/merge functionality inherited from LPS as well as resolving some longstanding limitations associated with the 3D Surface Tool (e.g. it dramatically increases the number of points that can be handled). For 9.2 we also added a few formats such as LAS and two flavors of ASCII (with and without point codes for breaklines). The 3D Surface Tool will likely remain available for a few more versions, until we have fully replaced it's functionality in the Terrain Prep Tool.

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.