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Using your knowledge of geography, geospatial and remote sensing science, and using the image classification tools in ArcGIS, you have produced a pretty good classified raster for your project area. Now it’s time to clean up some of those pesky pixels that were misclassified – like that one pixel labelled “shrub” in the middle of your baseball diamond. The fun part is using the Pixel Editor to interactively edit your classified raster data to be useful and accurate. The resulting map can be used to drive operational applications such as land use inventory and management.

 

For operational management of land use units, a useful classified map may not necessarily be the most accurate in terms of identified features. For example, a small clearing in a forest, cars in a parking lot, or a shed in a backyard are not managed differently than the larger surrounding land use. The Pixel Editor merges and reclassifies groups of pixels, objects and regions quickly and easily into units that can be managed similarly, and result in presentable and easy-to-understand maps for your decision support and management.

 

What is the Pixel Editor?

The Pixel Editor is an interactive group of tools that enables editing of raster data and imagery , and it is included with the ArcGIS Pro Image Analyst. It is a suite of image processing capability, driven by an effective user interface, that allows you to interactively manipulate pixel values. Try different operations using different parameter settings to achieve optimum editing results, then save, publish and share them.

 

The Pixel Editor is contextual to the raster source type of the layer being edited, which means that suites of capability are turned on or off depending on the data type of the layer you are working with. For thematic data, you can reassign pixels, objects and regions to different classes, perform operations such as filtering, shrinking or expanding classes, masking, or even create and populate new classes. Edits can be saved, discarded, and reviewed in the Edits Log.

 

Pixel Editor in action

Because the Pixel Editor is contextual, you need to first load the layer you want to edit. Two datasets are loaded into ArcGIS Pro, the infrared source satellite image and the classified result. The source data is infrared satellite imagery where vegetation is depicted in shades of red depending on coverage and relative vigor. This layer has been classified using the Random Trees classifier in ArcGIS Pro. The class map needs editing to account for classification discrepancies and to support operational land use management.

 

Launch the Pixel Editor

To launch the Pixel Editor, select the classified raster layer in the Contents pane, go to the Imagery tab and click the Pixel Editor button from the Tools group.


The Pixel Editor tab will open. In this example, we’ll be editing a land use map, so the editor will present you with editing tools relevant for thematic data.

The Reclassify dropdown menu

The Region group provides tools for delineating and managing a region of interest. The Edit group provides tools to perform specific operations to reclassify pixels, objects or regions of interest. The Edit group also provides the Operations gallery, which only works on Regions.

 

Reclassify

Reclassify is a great tool to reassign a group of pixels to a different class. In the example below, you can see from the multispectral image that either end of the track infield is in poor condition with very little vegetation, which resulted in that portion of the field being incorrectly classified. We want to reclassify these areas as turf, which is colored bright green in the classified dataset.

 

Infrared image and associated classmap needing edits.

We used the multispectral image as the backdrop to more easily digitize the field, then simply reassigned the incorrect class within the region of interest to the Turf class.

Edited classmap

Majority Filter and Expand
Check out the parking lots south of the track field containing cars, which are undesirable in terms of classified land use. We removed the cars and make the entire parking lot Asphalt with a two-step process:

Parking lot before editing
(1) We digitized the parking lot and removed the cars with a Majority Filter operation with a filter size of 20 pixels – the size of the biggest cars in the lot.

(2) Then we used Expand to reclassify any remaining pixels within the lot to Asphalt.

Parking lot after Majority Filter and Expand operations

Add a new class

Another great feature of the Pixel Editor is the ability to add a new class to your classified raster. Here, we added a Water class to account for water features that we missed in the first classification.

Add new class

New class WATER was added to the classmap

In the New Class drop-down menu, you can add a new class, provide its name, class codes, and define a color for the new class display.

After adding the new class to the class schema, we used the Reclass Object tool to reassign the incorrect Shadow class to the correct Water class. Simply click the object you want to reclassify and encompass it within the circle - and voila! – the object is reclassified to Water.

Reclass incorrect class "Shadow" to correct class "Water"

 

Feature to Region

Sometimes you may have an existing polygon layer with more accurate class polygon boundaries. These could be building footprints, roads, wetland polygons, water bodies and more. Using the Feature to Region option you can easily create a region of pixels to edit by clicking on the desired feature from your feature layers in the map. Then use the Reclass by Feature tool to assign the proper class.

Region from Feature Edit

We see the updated water body now matches the polygon feature from your feature class. The class was also changed from Shadow to its correct value, Water.

 

Summary

The Pixel Editor provides a fast, easy, interactive way to edit your classified rasters. You can edit groups of pixels and objects, and editing operations include reclassification using filtering, expanding and shrinking regions, or by simply selecting or digitizing the areas to reclassify. You can even add an entire new class. Try it out with your own data, and see how quickly you can transform a good classification data set into an effective management tool!

 

Acknowledgement

Thanks to the co-author, Eric Rice, for his contributions to this article.

Do you have blemishes in your image products, such as clouds and shadows that obscure interesting features, or DEMs that don’t represent bare earth? Or perhaps you want to obscure certain confidential features, or correct erroneous class information in your classmap. The Pixel Editor can help you improve your final image products.

 

After you have conducted your scientific remote sensing and image analysis, your results need to be presented to your customers, constituents and stakeholders. Your final products need to be correct and convey the right information for decision support and management. The pixel editor helps you achieve this last important aspect of your workflow – effective presentation of results.

 

Introducing the Pixel Editor

The Pixel Editor, in the Image Analyst extension, provides a suite of tools to interactively manipulate pixel values for raster and imagery data. It allows you to edit an individual pixel or groups of pixels. The types of operations that you can perform depends on the data source type of your raster dataset.

The Pixel Editor tools allows you to perform the following editing tasks on your raster datasets:

Blog Series

We will present a series of blogs addressing the robust capabilities of the Pixel Editor. We will focus on real-world practical applications for improving your imagery products, and provide tips and best practices for getting the most out of your imagery using the Pixel Editor. Stayed tuned for this interesting and worthwhile news.

 

Your comments, inputs and application examples of the Pixel Editor capability are very welcome and appreciated!

ArcGIS Enterprise configured for Raster Analytics enables large and small organizations to distribute and scale raster processing, storage and sharing to meet requirements for unique projects. This flexibility and elasticity also allows you to pursue projects that were previously out of reach due to hardware, software, personnel, or cost constraints. An overview of Raster Analytics concepts and advantages is described in the article Imagery Superpowers – Raster analytics expands imagery use in GIS.

Raster Analytics Processing Workflow

To help you become familiar with the benefits of Raster Analytics, Esri is offering a new Learn Lesson for ArcGIS Enterprise users. The lesson guides you through the process of configuring your Enterprise system for Raster Analytics, shows you how to use raster processing tools and functions to assess potential landslide risk associated with wildfire. The analysis is run on your distributed processing system, and the results are published to your Enterprise portal for ease of sharing across your organization. The lesson is a practical guide for implementing a Raster Analytics deployment, and demonstrating how standard ArcGIS Pro tools and functionality can be used to run distributed processes behind your firewall and in the cloud, and shared with stakeholders across your enterprise. Check out this story map, which gives you a more detailed overview of what the lesson involves.

Drag and drop tools into the function editor to create raster function chains.

Ready to try it out? If you want to extend your capabilities with Raster Analytics for increased productivity, test out the lesson and see why users are excited about the opportunity to address demanding projects in a more effective and efficient manner.

 

Many Thanks to Katy Nesbitt (knesbitt@esri.com) for co-authoring this article.

Raster analytics using ArcGIS Enterprise is a flexible raster processing, storage, and sharing system that employs distributed computing and storage technology. Use raster analytics to apply the rich set of raster processing tools and functions offered in ArcGIS, build your own custom functions and tools, or combine multiple tools and functions into raster processing chains to execute your custom algorithms on large collections of raster data. Source data and processed results are stored, published and shared across your enterprise accordingly.

 

This extensive capability can be further expanded by leveraging cloud computing capabilities and resources.  The net result: image processing and analysis jobs that used to take days or weeks can now be done in minutes or hours, and jobs that were impossibly large or too daunting are now within easy reach.

 

What can raster analytics do?

By leveraging ArcGIS Enterprise, raster analytics enables you to:

  • Quickly process massive imagery or raster datasets in a scalable environment
  • Execute advanced, customized raster analysis
  • Share results with individuals, departments, and organizations within or outside your enterprise

 

Raster analytics is ArcGIS Image Server configured for raster analysis in a processing and storage environment that maximizes processing speed and efficiency.  Built-in tools and functions cover preprocessing, orthorectification and mosaicking, remote sensing analysis, and an extensive range of math and trigonometry operators; your custom functions can extend the platform’s analytical capabilities even further.

 

Fully utilize your existing ArcGIS Image Server on-site, or exploit the elastic processing and storage capacity of cloud computing and storage platforms such as Amazon Web Services and Microsoft Azure to dynamically increase or reduce your capacity depending on the size and urgency of your projects.  The scalable environment of raster analytics empowers you to implement computationally intensive image processing that used to be out of reach or cost-prohibitive. This implementation saves you time, money, and resources.

 

Raster analytics is also designed to streamline and simplify collaboration and sharing. Users across your enterprise can contribute data, processing models, and expertise to your imagery project, and share results with individuals, departments, and organizations in your enterprise.

 

Finally, Raster analytics using ArcGIS Enterprise integrates your image processing and analysis with the world’s leading GIS platform, and allows users to seamlessly draw on the world’s largest collection of online digital maps and imagery.

 

How does raster analytics work?

ArcGIS Image Server configured for the role of raster analytics provides software and user interfaces to organize and manage your processing, storage, and sharing of raster and feature data, maps, and other geographic information on a variety of devices. This integrated system manages the dissemination of processing and storage of results (1) on-premises and behind the firewall for classified deployments, (2) in cloud processing and storage environments, or (3) a combination of both environments.

 

The foundation of raster analytics is ArcGIS Enterprise, which includes an Enterprise GIS Portal, ArcGIS Data Store, Image Server configured for raster analytics, raster data store and ArcGIS Web Adaptor. ArcGIS Enterprise integrates the components of the raster analytics system to support scalable, real-world workflows.

 

Scale your powerful processing and storage capabilities by deploying ArcGIS Enterprise in the cloud via Microsoft Azure or Amazon Web Services (AWS). For example, you can automatically scale capacity up and down according to conditions you define, or automatically dispense application traffic across multiple instances for better performance. ArcGIS Enterprise makes deployment easier by providing Cloud Builder for Microsoft Azure or AWS CloudFormation with sample templates to configure and deploy your system in the cloud.

 

Develop, test and optimize your raster processing chains using Esri’s rich set of more than 200 functions and tools in the familiar ArcGIS Desktop or web map viewer. Once verified and optimized in the dynamic on-the-fly processing environment, submit your processing chain to ArcGIS Portal, which manages the distribution of processing, storage, and publication of results.

 

The ideal deployment of raster analytics is comprised of three server sites to perform the primary roles of the portal host server, raster analysis server, and the image hosting server. Two licenses are required for raster analytics, ArcGIS Enterprise and Image Server.

Raster Analytics System Diagram

The hosting server is your portal’s server for standard portal administration and operations such as managing and dispensing processing, storage, and publication of results to raster analysis servers, image servers, and data stores.  It also hosts the ArcGIS Data Store for GIS data and allows users to publish data and maps to a wider audience as web services.

 

Raster analytics jobs are processed by image servers dedicated for raster analytics, comprised of one or more servers, each with multiple processing cores. The image processing and raster analytics tasks are distributed at the tile level or scene level depending on the tools and functions used. Raster analytics manages the processing results to either the ArcGIS Data Store on the hosting server for feature data products, or to the raster data store for imagery and raster data products. The raster data store can be implemented using distributed file share storage or using cloud storage such as Amazon S3 or Microsoft Azure blob storage.

 

The image hosting server hosts all the image services generated by the raster analysis server. It includes the raster data store configured with the Image Server Manager, which manages distributed file share storage and cloud storage of image services using Amazon S3 or Microsoft Azure blob storage. The image hosting server stores and returns results requested by members of your enterprise.

 

System configuration apps assign the roles of the servers and data stores, and also set the permission structure for all the users across your enterprise. This facilitates optimal flexibility in configuring and implementing your raster analytics system to address specific projects. Multiple servers can be scaled up for raster analytics processing and storage as required.

 

See the tutorial to set up a base ArcGIS Enterprise deployment.

 

More Information

To learn more about raster analytics using ArcGIS Enterprise and ArcGIS Image Server, check out this video.

Explore these help topics to get started with raster analytics:

To see how raster analytics is being used, check out the Chesapeake Conservancy and Distributed Image Processing presentation, or attend the Plenary session at the 2017 Esri User Conference in San Diego to hear about Chesapeake Conservancy’s experience processing and sharing the entire Chesapeake watershed using raster analytics.

 

Please plan to attend a few presentations addressing raster analytics at the 2017 Esri User Conference:

Raster Analytics at Esri UC2017

The June 2017 update of ArcGIS Online includes some useful capabilities for displaying imagery served by your image services. These capabilities give you greater control for visualizing the information contained in your image services. When we talk about rendering, we’re not talking about making soap out of fat. Here at Esri, rendering is the process of displaying your data. How an image service is rendered depends on what type of data it contains and what you want to show.

 

Once you search for and add a layer, and your image is displayed in Map Viewer, click the More Options icon then Display to open the Image Display pane.

Image Display Options

You see a new category named Image Enhancement. This is where the real fun begins.

Image Enhancement pane

The Symbology Type options include Unique Values, Stretch and Classify. Unique Values and Classify renderers work with single-band image services, while the Stretch renderer works on both single and multiple band images.

 

Unique Values Renderer

Unique values symbolize each value in the raster layer individually and are supported on single band layers with Raster Attribute table. The symbology can be based on one of more attribute fields in the dataset. The colors are read from the Raster Attribute table and if they are not available the renderer assigns a color to each value in your dataset. This symbology type is often used with single band thematic data, such as land cover, because of its limited number of categories. It can also be used with continuous data if you choose a color ramp that is a gradient.

Unique Values Renderer

  1. Use the Field drop-down to select the field you want to map. The field is displayed in the table.
  2. Click the Color Ramp drop-down and click on a color scheme. If your image service already has a color ramp, such as the NLCD service in this example, it is displayed by default.
  3. The colors in the Symbol column and Labels can be edited as required.
  4. Click Apply to display the rendering in the layer

 

Stretch

The stretch parameters improve the appearance of your image by adjusting the image histogram controlling brightness and contrast enhancements. Either single or multiple band images can be stretched. For multiple band images, the stretch is applied to the band combination previously chosen in the RGB Composite options. The stretch options enhance various ground features in your imagery to optimize information content.

1.   Click the Stretch Type drop-down arrow and choose the stretch type to use. The following contrast enhancements determine the range of values that are displayed.

  • None – No additional image enhancement will be performed
  • Minimum and Maximum – Displays the entire range of values in your image. Additional changes can be made by editing the values in the Min-Max grid (available only when Dynamic range adjustment is turned off.)
  • Standard Deviation – Display values between a specified number of standard deviations
  • Percent Clip – Set a range of values to display. Use the two text boxes to edit the top and bottom percentages.

2.   If the Stretch type is set to an option other than None, the following additional image enhancement options will be available.

  • Dynamic range adjustment – Performs one of the selected stretches, but limits the range of values to what is currently in the display window. This option is always turned on if the imagery layer does not have global statistics.
  • Gamma – Stretches the middle values in an image but keeps the extreme high and low values constant.

3.   For single-band layers, you can optionally choose a new color scheme from the Color Ramp drop-down menu after applying a stretch method on the layer.

4.   Click Apply to display the rendering in the layer.

Here’s a WorldView-2 natural color image of Charlotte, NC, using the default no stretch:

Multispectral Image, No Stretch

And here is the same imagery layer with the top 2% and bottom 20% of the histogram omitted:

Multispectral Imagery, Percent Stretch

Classify Renderer

Classify symbology is supported by single band layers. It allows you to group pixels together in a specified number of classes. The following are the different settings available with the Classify symbology.

  • Field – Represents the values of the data.
  • Method – Refers to how the break points are calculated.
  • Defined Interval – You specify an interval to divide the range of pixel values and the number of classes will be automatically calculated.
  • Equal Interval – The range of pixel values are divided into equally sized classes where you specify the number of classes.
  • Natural Breaks – The class breaks are determined statistically by finding adjacent feature pairs between which there is a relatively large difference in data value.
  • Quantile – Each class contains equal number of pixels.
  • Classes – Sets the number of groups.
  • Color Ramp – Allows you to choose the color ramp for displaying the data.

Classify symbology works with single band layers that have either a Raster Attribute Table or Histogram values. If a histogram is absent, it is generated when you select the symbology type.

 

Here’s the classified map of Charlotte, specifying 15 classes and using the Natural Breaks method for determining class breaks:

Class Map

Summary

These new Map Viewer image rendering capabilities are similar to what you are used to in ArcMap and ArcGIS Pro. Since this release, Scene Viewer also supports imagery layers, however we are still working on bringing the new Map Viewer image rendering capabilities into Scene Viewer. Check out these new imagery capabilities in ArcGIS Online and see how they can enhance the stories behind your data.

 

Please leave us comments below for any future enhancements you’d like to see. And check back in a few months; we have a lot of other cool stuff planned for imagery in upcoming releases.

Imagery can add valuable information and context to a wide array of GIS projects. For example, you can detect impervious surfaces for storm water management, map and manage riparian corridors, or track what’s changing in your county. Sometimes, though, incorporating imagery into your GIS can feel overwhelming—how can your system handle that much data?

 

Enter raster analytics, a distributed processing, storage, and sharing system designed to quickly process large collections of aerial, drone, or satellite imagery, then extract and share meaningful information for critical decision support. Raster analytics can be run locally, but you can also pair it with distributed cloud computing to maximize efficiency. Image processing and analysis jobs that used to take days or weeks can be completed in minutes or hours, bringing imagery projects that were impossibly large or daunting within reach.

 

Raster analytics leverages ArcGIS Enterprise, expanded with ArcGIS Image Server configured for distributed raster analysis, to integrate the components of the raster analytics system to support scalable, real-world workflows

 

What can raster analytics do?

By leveraging ArcGIS Enterprise with ArcGIS Image Server, raster analytics enables you to:

  • Quickly process massive imagery or raster datasets in a scalable environment
  • Execute advanced, customized raster analysis
  • Share results with individuals, departments, and organizations within or outside your enterprise

The scalable environment of raster analytics empowers you to perform computationally intensive image processing that would otherwise be out of reach or cost-prohibitive. When implemented on-site, raster analytics uses distributed processing to improve efficiency. You can also maximize efficiency by exploiting cloud platforms such as Amazon Web Services or Microsoft Azure, which allow you to dynamically increase or reduce your capacity based on the size and urgency of your projects.  Either implementation can save you time, money, and resources.

 

Raster analytics uses all the advanced image processing and analysis capabilities of ArcGIS Pro to maximum advantage. Built-in raster functions cover preprocessing, orthorectification and mosaicking, remote sensing analysis, and an extensive range of math and trigonometry operators, while your custom functions can extend the platform’s analytical capabilities even further.

Raster Analytics System Diagram

Raster analytics is also designed to streamline collaboration and sharing. Users across your enterprise can contribute data, processing models, and expertise to your imagery project, then share results with individuals, departments, and organizations in your enterprise.

 

Finally, raster analytics integrates your image processing and analysis with the world’s leading GIS platform, and allows users to seamlessly draw on Living Atlas of the World, the world’s largest collection of online digital maps and imagery.

 

How is raster analytics used today?

The Chesapeake Conservancy, working with the University of Vermont and WorldView Solutions, was tasked by the Chesapeake Bay Program to produce one-meter-resolution land cover maps covering 100,000 square miles of the Chesapeake Bay watershed. These high-resolution land cover maps, which classify natural and man-made landscape features, are crucial for supporting watershed and storm water management, conservation, and for reducing pollution into the bay.

 

To produce this essential dataset, the Chesapeake Conservancy needed to process over 20 terabytes of raster data and categorize it into twelve land cover types. This project took a daunting 18 months to complete using their local machine resources. As a result, Chesapeake Conservancy is now working with raster analytics in the cloud to make this timeline more efficient and cost-effective going forward.

As a proof of concept, they used raster analytics to produce a persistent one-meter land cover dataset of Kent County, Delaware (798 square miles). The Kent County project—comprised of more than 30GB and 3.8 billion pixels of raster data—ran on a ten-machine cluster, each with twenty cores, and completed in less than 5 minutes. This same job took days to to process on their local machines.

 

The Chesapeake Conservancy is now engaged in reprocessing the entire Chesapeake watershed to benchmark time and cost savings using raster analytics for the project. Using raster analytics for projects in the future will mean that the Chesapeake Conservancy can accomplish ambitious projects in a timely and cost-effective manner, without having to spend resources to acquire, configure, and maintain a large computing and storage infrastructure.

See the Chesapeake Conservancy and Distributed Image Processing presentation for more details, or check out the Plenary session at the 2017 Esri User Conference in San Diego to hear about Chesapeake Conservancy’s experience processing and sharing the entire Chesapeake watershed using raster analytics

 

More Information:

To learn more about raster analytics using ArcGIS Enterprise and ArcGIS Image Server, check out this video.

Explore these help topics to get started with raster analytics:

Please plan to attend a couple presentations addressing raster analytics at the 2017 Esri User Conference:

Raster Analytics at Esri UC2017