Available with Image Analyst license.
The ArcGIS Image Analyst extension provides functions, tools, and capabilities for image and geospatial analysts who focus on the following areas:
- Image interpretation and exploitation
- Creation of information products from imagery
- Advanced feature interpretation and measurements from imagery
- Detailed feature compilation and measurement on stereo imagery
- Advanced raster and image analysis workflows for machine learning and feature extraction
Image analysts extract data and information from imagery using manual and computer-assisted methods. The Image Analyst extension provides advanced capabilities to support both image exploitation methods.
Manual image interpretation applications include Stereo Mapping and Image Space Analysis. These applications support the collection of 3D feature data using standard feature creation and editing tools, saving feature class data in a geodatabase or as files, and sharing them in ArcGIS Enterprise.
Computer-assisted image exploitation includes advanced classification and a suite of raster functions and geoprocessing tools. Both functions and tools can be chained together into custom algorithms using raster functions templates and models, respectively. These processing chains can be deployed on the desktop or distributed processing environments in ArcGIS Enterprise either on-premises or through a portal.
The suite of functions, tools, and capabilities for advanced image analysis require the Image Analyst extension.
The capabilities, functions, and tools that are provided in the Image Analyst extension are geared toward image analysts who perform manual image interpretation, advanced remote sensing, and semiautomated image processing feature extraction. These image exploitation activities are grouped into the following functional categories:
- Stereo mapping—Visualize imagery and capture 3D feature data in a stereo viewing environment.
- Image classification—Perform object-based and traditional image analysis using image segmentation and classification tools and capabilities.
- Perspective imagery—Work with oblique imagery oriented in a natural perspective mode to facilitate effective image interpretation applications.
- Raster functions—Perform on-the-fly raster analysis and image processing on an extensive suite of remote sensing data types, and save your results if desired. Create raster function chains and deploy them on the desktop or in distributed processing and storage environments either on-premises or in the cloud.
- Geoprocessing tools—Perform remote sensing analysis and image processing using individual tools, and create and deploy them in processing models locally on the desktop or distributed processing and storage environments on-premises or in the cloud.
These capabilities, functions, and tools are described in more detail below.
With the stereo mapping capability you can compile 3D feature data in a stereo viewing and mapping system. This capability enables you to visually analyze imagery and accurately collect features of interest.
The stereo mapping capability in Image Analyst includes a stereo map viewer that displays and manipulates stereo image pairs from satellite, aerial, and drone sensor platforms. The stereo display supports multispectral, three-band, and panchromatic imagery, direct enhancement of imagery, superimposition of 3D GIS data on stereo imagery, zooming and roaming, and other image adjustments.
The photogrammetrically accurate 3D pointer measures and collects ground features directly into feature classes. Two types of 3D eyewear are supported: lightweight active shutter glasses and anaglyph cyan/red glasses.
The Stereo Map tab contains tools to set up, enhance, and manage stereo models, and superimpose vector GIS data on stereo imagery; ground feature measurement tools; and a stereo model manager. The standard feature creation and editing tools are used for a familiar experience to compile 3D features into feature classes. Newly created or updated features conform to your existing feature templates and maintain your topology, styles, attributes, and other feature elements when saved.
Image classification is one of the most effective and efficient ways to transform continuous imagery into categorical data and information for inventory and management of assets and land units. It is a computer-assisted approach to processing imagery, where the image analyst initiates steps and techniques for a classification method, and the computer executes the supporting computations. The analyst intercedes at critical junctures to make decisions that determine the type and characteristics of the classification results.
Two main types of classification approaches are supported, object-oriented classification and pixel-based classification. Object-oriented classification is based on image segmentation where adjacent pixels with similar multispectral or spatial characteristics are grouped into objects. These objects, sometimes called superpixels, represent partial or complete features and are processed using a variety of classifiers to produce a class map. Pixel-based classification follows a similar process, where pixels are classified into categories defined by the analyst.
Supported classifiers include both traditional and advanced machine-learning approaches. Traditional classifiers are based on statistical-based methods such as unsupervised isocluster and supervised maximum likelihood classification. Advanced classifiers are based on sophisticated machine-learning methods including random trees, support vector machine, and deep learning.
Once imagery is initially classified, the accuracy is assessed and the class map is refined to correct either the class categories or regions within the class map in an iterative manner. Accuracy assessment can be performed on both the input training data and the resulting class map output.
The classification process usually requires several steps to progress from properly preprocessing the imagery, assigning the class categories and creating relevant training data, executing the classification, and assessing and refining the accuracy of results. The Classification Wizard guides the analyst through the classification workflow and helps ensure acceptable results.
The class map, with its associated symbology, can be saved or converted to a GIS vector file with an associated attribute table.
Image Space Analysis
Imagery is often collected at significant angles, referred to as oblique imagery. It is useful for ascertaining information about features such as buildings, bridges, towers, and engineering infrastructure that is not obtainable from vertical imagery. Satellite imagery is often collected at angles greater than 15 degrees off-nadir, as is aerial and drone imagery. Displaying oblique imagery in a map projection system causes buildings and other ground features to appear to lean at a variety of disorienting angles, making oblique imagery hard to interpret. It can also be severely distorted by rectifying it to fit the map projection.
ArcGIS Pro enables viewing and working with oblique imagery in perspective mode by displaying it in an easy to understand manner. It is displayed with buildings and features oriented vertically up toward the top of the display, which better enables image interpretation applications. Perspective mode displays images in image space (in columns and rows) rather than map space (in a map projection system) by using an image coordinate system (ICS). The ICS facilitates the seamless transformation between image space and map space and allows additional image and GIS layers to be properly registered to the imagery. The ICS uses the metadata containing image orientation and position information, along with other pertinent information about how and when the image was collected, to support the transformation between image space and map space. Enabling imagery in image space in the map view is referred to as perspective mode.
Oblique imagery contains information not available from vertical imagery, such as building facades, points of ingress and egress, profiles of features and objects, and more. Oblique imagery displayed in perspective mode is useful for manual image interpretation applications and for collecting and recording information about features. An important capability of oblique imagery is the ability to create and edit features in image space and save them in a map projection of choice. Additionally, features can be interactively measured in perspective mode, and results are displayed and recorded in your units of choice.
Image analysts and remote sensing professionals frequently develop and deploy their own image processing chains and algorithms tailored for specific applications and data sets. While workflows may be generally well defined, analysts often need to adjust and refine parameter settings, depending on physical, atmospheric, environmental, and data characteristics. Raster functions provide a flexible and powerful way to develop and refine image processing workflows.
Raster functions are dynamic operations that apply on-the-fly processing directly to the image pixels in the display. You see your image processing results immediately as you roam and zoom in and out on the imagery in your display. Since no intermediate datasets are created, processes and adjustments to processing parameters can be applied quickly. The results are not saved to a file on disk unless you explicitly specify that the results are to be persisted.
Raster functions can be combined into function chains that can be saved as raster function templates using the Function Editor. Raster function templates can also be shared to members of your enterprise, and run in distributed processing environments on-premises or in your web-enabled ArcGIS Enterprise deployment.
An extensive list of raster functions is provided with the Image Analyst extension. These functions are grouped into categories of related functionality in the following table. Each function is linked in the table to its detailed description.
Image Analyst function categories
Use the segmentation and classification functions to prepare segmented rasters or pixel-based raster datasets to use in creating classified raster datasets.
The general math functions apply mathematical functions to the input rasters. These tools fall into several categories. The arithmetic tools perform basic mathematical operations, such as addition and multiplication. There are tools that perform various types of exponentiation operations, which include exponentials and logarithms, in addition to the basic power operations. The remaining tools are used either for sign conversion or for conversion between integer and floating point data types.
The conditional math functions allow you to control the output values based on the conditions placed on the input values. The conditions that can be applied are of two types, queries on the attributes or a condition based on the position of the conditional statement in a list.
The logical math functions evaluate the values of the inputs and determine the output values based on Boolean logic. These functions process raster datasets in five main areas: Bitwise, Boolean, Combinatorial, Logical, and Relational.
The trigonometric math functions perform various trigonometric calculations on the values in an input raster.
Use the statistics functions to perform statistical raster operations on a local, neighborhood, or zonal basis.
The Weighted Sum function allows you to overlay several rasters, multiplying each by their given weight and summing them together.
As noted above, image analysts and remote sensing professionals often develop and deploy their custom processing workflows for particular applications. These professionals can combine geoprocessing tools into geoprocessing models, similar to raster functions templates (RFTs). The main difference between geoprocessing models and RFTs is that the results from a geoprocessing model are always saved to disk. Models can also be shared to members of your enterprise and deployed in distributed processing environments on-premises or in the cloud with ArcGIS Enterprise.
An extensive list of geoprocessing tools is provided with the Image Analyst extension. These tools are grouped into categories of related functionality in the following table. Each tool is linked in the table to its detailed description.
Image Analyst geoprocessing toolsets
Perform traditional or advanced machine learning image classification on segmented or pixel-based imagery.
The conditional math tools allow you to control the output values based on the conditions placed on the input values. The conditions that can be applied are of two types, queries on the attributes or a condition based on the position of the conditional statement in a list.
Map algebra is a way to perform spatial analysis by creating expressions in an algebraic language. With the Raster Calculator tool, you can easily create and run map algebra expressions that output a raster dataset.
The general math tools apply a mathematical function to the input. These tools fall into several categories. The arithmetic tools perform basic mathematical operations, such as addition and multiplication. There are tools that perform various types of exponentiation operations, which include exponentials and logarithms in addition to the basic power operations. The remaining tools are used either for sign conversion or for conversion between integer and floating point data types.
The logical math tools evaluate the values of the inputs and determine the output values based on Boolean logic. The tools are grouped into five main categories: Bitwise, Boolean, Combinatorial, Logical, and Relational.
The trigonometric math tools perform various trigonometric calculations on the values in an input raster.
Use the Statistical tools to perform statistical raster operations on a local, neighborhood, or zonal basis.
The Overlay tools allows you to overlay several rasters and perform various operations on them.