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(55 Posts)
EmilyWindahl
Esri Contributor

Users of the Oriented Imagery Catalog Management Tools in ArcGIS Pro 2.5 may have encountered a crash when browsing for an Oriented Imagery Catalog (OIC) as input in any of the tools in the Oriented Imagery Catalog toolbox. 

 

This bug will be fixed in the next release of ArcGIS Pro, but there is a workaround in the meantime. To avoid the crash, don't click the Browse folder icon to navigate to your OIC. Instead of browsing to the file, you should copy the path to the OIC file and paste it into the input field of the GP tool.

To do this in Windows:

  1. Open Windows File Explorer.
  2. Browse to the OIC file. (If you’ve created this in your project’s geodatabase, the OIC file will be located by default at C:\Users\[username]\Documents\ArcGIS\Projects\[Project Name]\[OIC name].)
  3. Select the OIC file, then click Copy Path. (You may have to remove any quotation marks around the file path.)

   Screenshot of Windows File Explorer

4. In ArcGIS Pro, paste the path into the Input Oriented Imagery field of the GP tool.

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VinayViswambharan
Esri Contributor

The ArcGIS Image Analyst extension for ArcGIS Pro 2.5 now features expanded deep learning capabilities, enhanced support for multidimensional data, enhanced motion imagery capabilities, and more.

Learn about  new imagery and remote sensing-related features added in this release to improve your image visualization, exploitation, and analysis workflows.

Deep Learning

We’ve introduced several key deep learning features that offer a more comprehensive and user-friendly workflow:

  • The Train Deep Learning Model geoprocessing tool trains deep learning models natively in ArcGIS Pro. Once you’ve installed relevant deep learning libraries (PyTorch, Fast.ai and Torchvision), this enables seamless, end-to-end workflows.
  • The Classify Objects Using Deep Learning geoprocessing tool is an inferencing tool that assigns a class value to objects or features in an image. For instance, after a natural disaster, you can classify structures as damaged or undamaged.
  • The new Label Objects For Deep Learning pane provides an efficient experience  for managing and  labelling training data. The pane also provides the option to export your deep learning data.
  • A new user experience lets you interactively review deep learning results and edit classes as required.
New deep learning tools in ArcGIS Pro 2.5

New deep learning tools in ArcGIS Pro 2.5

Multidimensional Raster Management, Processing and Analysis

New tools and capabilities for multidimensional analysis allow you to extract and manage subsets of a multidimensional raster, calculate trends in your data, and perform predictive analysis.

New user experience

A new contextual tab in ArcGIS Pro makes it easier to work with multidimensional raster layers or multidimensional mosaic dataset layers in your map.

Intuitive user experience to work with multidimensional data

Intuitive user experience to work with multidimensional data

  • You can Intuitively work with multiple variables and step through time and depth.
  • You have direct access to the new functions and tools that are used to manage, analyze and visualize multidimensional data.
  • You can chart multidimensional data using the temporal profile, which has been enhanced with spatial aggregation and charting trends.

New tools for management and analysis

The new multidimensional functions and geoprocessing tools are listed below.

New geoprocessing tools for management

We’ve added two new tools to help you extract data along specific variables, depths, time frames, and other dimensions:

  • Subset Multidimensional Raster
  • Make Multidimensional Raster layer

New geoprocessing tools for analysis

  • Find Argument Statistics allows you to determine when or where a given statistic was reached in multidimensional raster dataset. For instance, you can identify when maximum precipitation occurred over a specific time period.
  • Generate Trend Raster estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster. For example, you might use this to understanding how sea surface temperature has changed over time.
  • Predict Using Trend Raster computes a forecasted multidimensional raster using the output trend raster from the Generate Trend Raster tool. This could help you predict the probability of a future El Nino event based on trends in historical sea surface temperature data.

Additionally, the following tools have improvements that support new analytical capabilities:

New raster functions for analysis

  • Generate Trend
  • Predict Using Trend
  • Find Argument Statistics
  • Linear Spectral Unmixing
  • Process Raster Collection

New Python raster objects

Developers can take advantage of new classes and functions added to the Python raster object that allow you to work with multidimensional rasters

New classes include:

  • ia.RasterCollection – The RasterCollection object allows a group of rasters to be sorted and filtered easily and prepares a collection for additional processing and analysis.
  • ia.PixelBlock – The PixelBlock object defines a block of pixels within a raster to use for processing. It is used in conjunction with the PixelBlockCollection object to iterate through one or more large rasters for processing.
  • ia.PixelBlockCollection – The PixelBlockCollection object is an iterator of all PixelBlock objects in a raster or a list of rasters. It can be used to perform customized raster processing on a block-by-block basis, when otherwise the processed rasters would be too large to load into memory.

New functions include:

  • ia.Merge() – Creates a raster object by merging a list of rasters spatially or across dimensions.
  • ia.Render (inRaster, rendering_rule={…}) – Creates a rendered raster object by applying symbology to the referenced raster dataset. This function is useful when displaying data in a Jupyter notebook.
  • Raster functions for arcpy.ia – You can now use almost all of the raster functions to manage and analyze raster data using the arcpy API
New tools to analyse multidimensional data

New tools to analyse multidimensional data

Motion Imagery

This release includes enhancements to our motion imagery support, so you can better manage and interactively use video with embedded geospatial metadata:

  • You can now enhance videos in the video player using contrast, brightness, saturation, and gamma adjustments. You can also invert the color to help identify objects in the video.
  • Video data in multiple video players can be synchronized for comparison and analysis.
  • You can now measure objects in the video player, including length, area, and height.
  • You can list and manage videos added to your project with the Video Feed Manager.
Motion imagery in ArcGIS Pro

Pixel Editor

The Pixel Editor provides a suite of tools to interactively manipulate pixel values of raster and imagery data. Use the toolset for redaction, cloud and noise removal, or to reclassify categorical data. You can edit an individual pixel or a group of pixels at once. Apply editing operations to pixels in elevation datasets and multispectral imagery. Key enhancements in this release include the following:

  • Apply a custom raster function template to regions within the image
  • Interpolate elevation surfaces using values from the edges of a selected region

Additional resources

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JuliaLenhardt
Esri Contributor

The ArcGIS Pro 2.3.2 software patch enables mosaic datasets created or modified by Pro 2.3 and 10.7 to be read and modified by earlier versions (ArcGIS Pro 2.1 and 10.5 or later).

 

If you created or modified a mosaic dataset using Pro 2.3 or 10.7, you can update it and make it compatible with earlier versions by following the steps below.

 

  1. Open Pro 2.3.2.
  2. In the Catalog pane, navigate to your mosaic dataset. Right-click and select Properties from the drop-down menu.
  3. Click Defaults which displays Image Properties. Scroll down to Maximum Number of Rasters Per Mosaic, and change the value to any number. Press <Tab> to update the field.
  4. Change the Maximum Number of Rasters Per Mosaic property back to the original value and press <Tab> to update the field again. 

This resets the mosaic dataset object to the new Pro 2.3.2 version.

Update to ArcGIS Pro 2.3.2 by going to My Esri or by using the in-app software updater.

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JuliaLenhardt
Esri Contributor

If you create or modify a mosaic dataset in Pro 2.3, it can only be read and modified by ArcGIS Pro 2.3 and ArcMap 10.7 and served with ArcGIS Image Server 10.7 or newer. If you intend to publish your mosaic dataset to an image server prior to 10.7, do not create or edit it using Pro 2.3.

 

Note that for ArcGIS Pro 2.3, significant changes were made to the internal structure of the mosaic dataset so once modified using Pro 2.3, the updated mosaic dataset cannot be read on older versions.

 

In general, mosaic datasets created with older versions of ArcGIS can be read and handled with newer versions of ArcGIS. However, a mosaic dataset created with a newer version of ArcGIS may not be backwards compatible with older versions.

 

See the table below for mosaic dataset compatibility:

 Mosaic Dataset compatibility between versions

 

Users utilizing a mosaic dataset created with a new version that does not use any new features in that version, have been able to read a mosaic dataset with an older version.  However, this may cause incompatibility issues.

Solution

The ArcGIS Pro 2.3.2 software patch enables mosaic datasets created or modified by Pro 2.3 and 10.7 to be read and modified by earlier versions (ArcGIS Pro 2.1 and 10.5 or later). Read more about it by clicking here.

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CodyBenkelman
Esri Regular Contributor

Do you have imagery from an aerial photography camera (whether a modern digital camera or scanned film) and the orientation data either by direct georeferencing or the results of aerial triangulation? If yes, you’ll want to work with a mosaic dataset, and load the imagery with the proper raster type.

The mosaic dataset provides the foundation for many different use cases, including:

  • On-the-fly orthorectification of images in a dynamic mosaic, for direct use in ArcGIS Pro or sharing through ArcGIS Image Server.
  • Production of custom basemaps from source imagery.
  • Managing and viewing aerial frame imagery in stereo
  • Accessing images in their Image Coordinate System (ICS).  


There are different raster types that support the photogrammetric model for frame imagery.  If you have existing orientation data from ISAT or Match-AT, you can use the raster types with those names to directly load the data (see
Help here). 

For a general frame camera, you’ll want to know how to use the Frame Camera raster type and we have recently updated some helpful resources:  

UI for automated script

Further information:

  • Note that if your imagery is oblique, the Frame Camera raster type supports multi-sensor oblique images. Refer to the http://esriurl.com/FrameCameraBestPractices for configuration advice.
  • If you want to extract a digital terrain model (DTM) from the imagery, or improve the accuracy of the aerial triangulation, see the Ortho Mapping capabilities of ArcGIS Pro (advanced license). http://esriurl.com/OrthoMapping.
  • If you are seeking additional detail on the photogrammetric model used within the Frame Camera raster type, see this supplemental document http://esriurl.com/FrameCameraDetailDoc

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JuliaLenhardt
Esri Contributor

In Part I of 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.

 

Ortho Mapping blog series part 2

 

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 the Terrain image 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

 

  1. 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.

Ortho Mapping Adjustment Options and GCP Import

 

 

  1. 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

 

Add tie points for each GCP and change some to check points

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.

 

  1. 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

 

  1. 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.
QA/QC outputs in the ortho mapping workspace

 

  1. 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.

Now what?

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 the Manage GCPs pane, or try modifying some of the Adjustment 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.

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