Given the growing number of people using commercial drones these days, a common question is: “What do I do with all this imagery?”
The simple answer is that it depends on what you’re trying to accomplish.
If you just want to share the imagery as-is, and aren’t worried about making sure it’s georeferenced to be an accurate depiction of the ground, Oriented Imagery is probably your answer. If you’re capturing video, Full Motion Video in the Image Analyst extension for ArcGIS Pro is your best bet. Ultimately, though, many users plan to turn the single frame images acquired by drones into authoritative mapping products—orthorectified mosaics, digital surface models (DSMs), digital terrain models (DTMs), 3D point clouds, or 3D textured meshes.
Esri has three possible solutions for producing authoritative mapping products from drone imagery, each targeted for different users— (1) Drone2Map for ArcGIS, (2) the ortho mapping capability of ArcGIS Pro Advanced, and (3) the Ortho Maker app included with ArcGIS Enterprise. Read on to get an overview of all three solutions, and to figure out which one is best for your application.
Drone2Map for ArcGIS
For individual GIS users, Drone2Map is an easy-to-use, standalone app that supports a complete drone-processing workflow.
Drone2Map includes guided templates for creating orthorectified mosaics and digital elevation models. It’s also the only ArcGIS product that creates 3D products from drone imagery, including RGB point clouds and 3D textured meshes. Once you’ve processed your imagery, it’s easy to share the final products—2D web maps and 3D web scenes can be easily published on ArcGIS Online with a single step. ArcGIS Desktop isn’t required to run Drone2Map, but products created with Drone2Map are Desktop-compatible. That’s important, because it gives you the option to use ArcGIS Pro as an image management solution, or to serve your imagery products as dynamic image services using ArcGIS Image Server.
Ortho mapping capability of ArcGIS Pro Advanced
For GIS professionals, the ortho mapping capability of ArcGIS Pro Advanced enables you to create orthomosaics and digital elevation models from drone images (as well as from modern aerial imagery, historical film, and satellite data) in the familiar ArcGIS Desktop environment.
There are added benefits to processing your drone imagery in ArcGIS Pro. For users with very large imagery collections, Pro’s image management capabilities are especially valuable. Managing drone imagery using mosaic datasets makes it easy to query images and metadata, mosaic your imagery, and build footprints. Image management and processing workflows in ArcGIS Pro can also be automated using Python or Model Builder. Finally, sharing your imagery is straightforward. While you can publish your products to ArcGIS Online, you can also use ArcGIS Pro in conjunction with ArcGIS Image Server to publish drone products as dynamic image services.
Ortho Maker app in ArcGIS Enterprise 10.6.1+
For ArcGIS Enterprise users, the Ortho Maker app offers a solution for organizations with multiple users who want simple, web-based workflows to create orthomosaics and DEMs from drone imagery.
Ortho Maker provides an easy-to-use web interface for uploading drone imagery and managing the ortho mapping workflow, while behind the scenes it uses the distributed processing and storage capability of Enterprise and ArcGIS Image Server to quickly process even very large collections of drone imagery. (That also means it requires ArcGIS Image Server configured for raster analysis.) The ArcGIS API for Python can be used to automate the ortho mapping process. Sharing Ortho Maker products is virtually automatic—they become imagery layer items accessible in your Enterprise portal, easily shared with users throughout your organization.
InPart Iof this blog series, we explained what an ortho mapping workspace is and how to create one for digital aerial imagery. At this point, the imagery has been organized and managed so that we can access all the necessary metadata, information, tools and functionality to work with our imagery, but we haven’t yet performed a bundle block adjustment.
Block adjustment is the process of adjusting the parameters in the image support data to get an accurate transformation between the image and the ground. The process is based on the relationship between overlapping images, control points, the camera model, and topography – then computing a transformation for the group of images (a block). With aerial digital data, it consists of three key components:
Tie points – Common points that appear in overlapping images, tying the overlapping images to each other to minimize misalignment between the images. These are automatically identified by the software.
Ground control points – These are usually obtained with ground survey, and they provide references from features visible in the images to known ground coordinates.
Aerial triangulation – Computes an accurate camera model, ground position (X, Y, Z), and orientation (omega, phi, kappa) for each image, which are necessary to transform the images to match the control points and the elevation model.
When we created our workspace, we provided the Frames and Cameras tables, which contain the orientation and camera information needed to make up our camera model and to establish the relationship between the imagery and the ground. We also provided an elevation model which we obtained from theTerrainimage service available through the Living Atlas of the World. Now we’re ready to move on to the next step in the ortho mapping process.
Performing a Block Adjustment for Digital Aerial Data
In the ortho mapping workspace, open the Ortho Mapping tab and select Adjustment Options from the Adjust group. This is where we can define the parameters used in computing the block adjustment, which includes computing tie points. For more information on each parameter, check out the Adjustment Options help documentation.
Next, we want to add Ground Control Points (GCPs) to our workspace to improve the overall georeferencing and accuracy of the adjustment. To do this, select the Manage GCPs tool in the Ortho Mapping tab and choose Import GCPs. We have a CSV table with X, Y and Z coordinates and accuracy to be used for this analysis.
If you have an existing table of GCPs, use this Import option and map the fields in the Import GCPs dialog for the X, Y, and Z coordinates, GCP label, and accuracy fields in your table. You may have photos of each GCP location for reference – if so, you can import the folder of photos for reference when you are measuring (or linking) the GCPs to the overlapping images.
You may also have secondary GCPs, or control points that were not obtained in a survey but from an existing orthoimage with known accuracy. You can import those here as well, or you can manually add them using the GCP Manager.
Once you have added GCPs to the workspace, use the GCP Manager to add tie points to the associated locations on each overlapping image. Select one of the GCPs in the GCP Manager table, then iterate through the overlapping images in the Image list below and use your cursor to place a tie point on the site that is represented by the GCP
A few notes:
Check Points: Be sure to change some of your GCPs to Check Points (right-click on the GCP in the GCP Manager and select “Change to Check Point) so you can view the check point deviation in the Adjustment Report after running the adjustment. This is essentially changing the point from a control point that facilitates the adjustment process to a control point that assesses the adjustment results.The icon in the GCP table will change from a circle to a triangle, and the check points appear as pink triangles in the workspace map.
Drone imagery: If you are performing a block adjustment with drone imagery, you must run the Adjust tool before adding GCPs. In this blog, we’re focusing on aerial digital data.
Finally, we click the Adjust tool to compute the block adjustment. This will take some time – transforming a number of images so that they align with each other and the ground is complicated work – so get up, maybe do some stretches or get yourself a cup of coffee. The log window will let you know when the process is complete. When the adjustment is finished, you’ll see new options available in the ortho mapping tab that enable you to assess the results of the adjustment.
Assessing the Block Adjustment
Run the Analyze Tie Points tool to generate QA/QC data in your ortho mapping workspace. The Overlap Polygons feature class contains control point coverage in areas where images overlap, and the Coverage Polygons feature class contains control point coverage for each image in the image collection. Inspect these feature classes to identify areas that need additional control points to improve block adjustment results.
Open the Adjustment Report to view the components and results of the adjustment report. Here you will find information about the number of control points used in the adjustment, the average residual error, tie point sets, and connectivity of overlapping imagery. In our case, the Mean Reprojection Error of our adjustment is 0.38 pixels.
The block adjustment tools allow for an iterative computation, so that you can check on the quality of the adjustment, modify options, add or delete GCPs, or recompute tie points before re-running the adjustment. If you are unsatisfied with the error in the Adjustment Report, try adding GCPs in theManage GCPspane, or try modifying some of theAdjustment Options. You can also change some of your check points back into GCPs, and choose a few other GCPs to be your check points. Re-run the adjustment and see how this impacts the shift.
Once you are satisfied with the accuracy of your adjusted imagery, it’s time to make ortho products! Check out the final installment in our blog series to see how it’s done.
Any remote sensing image, whether it’s a drone image, aerial photograph, or data from a satellite sensor, will inherently be impacted by some form of geometric distortion. The shape of local terrain, the sensor angle and altitude, the motion of the sensor system, and the curvature of the Earth all make it difficult to represent three dimensional ground features accurately in a two dimensional map. Image orthorectification corrects for these types of distortion so you can have a measurable, map-accurate image.
Everyone working with GIS data to make well-informed decisions needs up-to-date information about the natural, man-made, and cultural features on the ground: roads, land cover types, buildings, water bodies, and other features that fill the landscape. Much of the vector data that describes these features was actually created from orthorectified imagery, and can be combined with new imagery to update your landbase.
Esri’s Ortho Mapping suite enables you to orthorectify your remote sensing imagery to make it map-accurate. It also makes it easy to create other products like orthomosaics (mosaicked images corrected for distortion) and digital elevation models (terrain or surface models) which can be used as basemaps, part of a landbase, or for further analysis in stereo models, 3D analysis and feature extraction.
The workflow to create ortho mapping products will be presented in a three-part blog series, each with a short video:
Creating a workspace
Performing a block adjustment
Creating ortho mapping products
Let's get started!
Creating an Ortho Mapping Workspace
The first step in any project is getting organized – and creating an Ortho Mapping Workspace in ArcGIS Pro makes this easy to do.
TheOrtho Mapping Workspaceis a sub-project in ArcGIS Pro; it’s the interface you work with when interacting with ortho mapping workflows. The workspace is defined by the type of imagery you are working with (drone, aerial or satellite). In turn, the workspace is integrated with the tools and wizards to properly guide you through each step in the workflow. When you create a new workspace, an Ortho Mapping folder appears in your project folder structure in Catalog, and a new table of contents list view allows you toList By Ortho Mapping Entities. Again, the types of feature classes and tables you see in the Contents pane depend on the type of imagery you are working with.
Similar to Maps or Scenes within a project, a workspace is an object stored in the folder structure of a project and it can be accessed by other projects. All the feature classes and tables needed to orthorectify your imagery are created and managed in the workspace.
5 Simple Steps
Step 1: Open theImagerytab in your ArcGIS Pro project. This is where you can analyze and manage any raster data you want to work with in Pro. In the Ortho Mapping group, you’ll see theNew Workspacemenu that allows you to create a New Ortho Mapping Workspace, add an existing Ortho Mapping Workspace with a reference to that workspace, or import an Ortho Mapping Workspace by creating a copy of an existing workspace and storing the new copy in your project. SelectNew Workspace.
Step 2: TheNew Ortho Mapping Workspacewizard appears. Here you’ll give your workspace a name (required) which identifies your project in the Contents and Catalog panes. You can also provide a description (optional) and you’ll select the type of imagery you want to import. In our workflow, we’re using aerial imagery acquired byVexcel Imaging covering an area over Hollywood, California, so we’ll selectAerial – Digitalas the type. Click Next.
Step 3:TheImage Collectionpage opens. Here you’ll enter specific information about the type of sensor used to collect your imagery. You can choose from MATCH-AT, ISAT, or Applanix, or you can select the Generic Frame Camera, which requires you to provide the exterior and interior orientation information with the Frames tables and Cameras tables, respectively. Entering the Frames and Cameras information will provide the information necessary to correct for sensor-related distortion.
TheFrames tablehas aspecific schemathat is required in the ortho mapping workspace for aerial imagery. It contains the exterior orientation and other information specific for each image comprising your image collection. TheCameras tablecontains all the camera calibration information for computing the interior orientation, but you can add the camera information manually in the wizard or as a table. To edit the Camera parameters, you can hover over the Camera ID and click the Edit Properties button. You’ll also need to specify theFrame Spatial Reference, which be provided with your data.
In this workflow, we used the exterior orientation information that was provided along with our source imagery to create the Frames table in the necessary schema. We then pointed to a table that has the information for one camera, with CameraID = 0 (see the screen shot below - there's a check mark next to the 0 under Cameras).
*We are updating this for ArcGIS Pro 2.3 to be more user-friendly for a better experience!
Step 4: To correct for terrain displacement, you need to include an elevation source. The cool thing about working with the ArcGIS platform is you can access the thousands of maps, apps and data layers available in theArcGIS Living Atlas, so if you don’t have your own elevation data you can search for one and use it into your project. Here’s what we did:
In our ArcGIS Pro project, zoom to the area of interest in Hollywood.
On the Map tab, click Add Data and add data to the map.
Select the Living Atlas option under the Portal group and search for "Terrain." Add the Terrain imagery layer. At first, you might not be able to see much variation in the terrain. Click on the Appearancetab under theImage Service Layergroup and selectDRA(Dynamic Range Adjustment) to stretch the terrain imagery in the extent you are viewing.
In the Contents pane, right-click on the Terrain imagery layer and select Data > Export Raster.
In the Export Raster settings, specify the output raster dataset and set the Clipping Geometry to the Current Display Extent.
Now we can add our new DEM to the workspace. To do this, open theData Loader Optionspane in theImage Collection page. Click the browse button to navigate to the DEM created above, or use your own DEM.
Step 5:Finally, we left all the other values as default and clicked Finish.
A Logwindow will tell you how the creation of the workspace is coming along, and if there are any problems, an error message will be displayed. When it’s complete, you’ll see the newOrtho Mapping Entitiesin yourContentspane: various control points including Ground Control Points, Check Points and Tie Points, the mosaic dataset that was created using your source data, and placeholders for Data Products, Solution Data, and QA/QC Data that haven’t been created yet.
Make sure to zoom and pan around the map to check out your Image Collection. With the Image Collection selected in theContentspane, you can open theDatatab from theMosaic Layercontext menu. Here you can change theSortandOverlapoptions for your mosaic dataset. We recommend using the Closest to Center or Closest to Nadir options for viewing.
Now that you have all your ortho mapping components organized in your workspace, the next step is to block adjust your data to make sure it’s map-accurate. Stay tuned for the next part of this blog series,Ortho Mapping with Aerial Data Part II: Getting Adjusted, where we’ll show you how to perform a block adjustment to make sure your data is ready for product generation and stereo compilation!
We showed you how to set up an ortho mapping workspace for aerial imagery. For an example of how to set up an ortho mapping workspace for satellite data, check out this short video!