List of ArcGIS Image Analyst geoprocessing tools—ArcGIS Pro

The ArcGIS Image Analyst extension provides a rich suite of geoprocessing tools in ArcGIS Pro.

Geoprocessing tools

A large number of geoprocessing tools are provided with the Image Analyst extension. These tools are grouped into categories of related functionality in the following table and associated toolsets.

Change Detection

The Change Detection toolset contains tools that perform change detection between raster datasets.

Tools in the Change Detection toolset
Tool	Description
Analyze Changes Using CCDC	Evaluates changes in pixel values over time using the Continuous Change Detection and Classification (CCDC) method and generates a change analysis raster containing the model results.
Analyze Changes Using LandTrendr	Evaluates changes in pixel values over time using the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method and generates a change analysis raster containing the model results.
Compute Change Raster	Calculates the absolute, relative, categorical, or spectral difference between two raster datasets.
Detect Change Using Change Analysis Raster	Generates a raster containing pixel change information using the output change analysis raster from the Analyze Changes Using CCDC tool or the Analyze Changes Using LandTrendr tool.

Classification and Pattern Recognition

The Classification and Pattern Recognition tools find, identify, and quantify patterns in imagery data. You can perform classic statistical and advanced machine learning image classification and regression analysis on segmented and pixel-based raster datasets. Additional tools are provided to perform training set and classification accuracy and refinement of class maps. The following table lists the available Classification and Pattern Recognition tools and provides a brief description of each.

Tools of the Classification and Pattern Recognition toolset
Tool	Description
Classify Raster	Classifies a raster dataset based on an Esri classifier definition file (.ecd) and raster dataset inputs. The .ecd file contains all the information needed to perform a specific type of Esri-supported classification. The inputs to this tool must match the inputs used to generate the required .ecd file.
Classify Raster Using Spectra	Classifies a multiband raster dataset using spectral matching techniques. The input spectral data can be provided as a point feature class or a .json file.
Compute Confusion Matrix	Computes a confusion matrix with errors of omission and commission and derives a kappa index of agreement, Intersection over Union (IoU), and an overall accuracy between the classified map and the reference data.
Compute Segment Attributes	Computes a set of attributes associated with the segmented image. The input raster can be a single-band or 3-band, 8-bit segmented image.
Create Accuracy Assessment Points	Creates randomly sampled points for postclassification accuracy assessment.
Generate Training Samples From Seed Points	Generates training samples from seed points, such as accuracy assessment points or training sample points. A typical use case is generating training samples from an existing source, such as a thematic raster or a feature class.
Inspect Training Samples	Estimates the accuracy of individual training samples. The cross validation accuracy is computed using the previously generated classification training result in an .ecd file and the training samples. Outputs include a raster dataset containing the misclassified class values and a training sample dataset with the accuracy score for each training sample.
Linear Spectral Unmixing	Performs subpixel classification and calculates the fractional abundance of different land-cover types for individual pixels.
Predict Using Regression Model	Predicts data values using the output from the Train Random Trees Regression Model tool.
Remove Raster Segment Tiling Artifacts	Corrects segments or objects cut by tile boundaries during the segmentation process performed as a raster function. This tool is helpful for some regional processes, such as image segmentation, that have inconsistencies near image tile boundaries. This processing step is included in the Segment Mean Shift tool. It should only be used on a segmented image that was not created from that tool.
Segment Mean Shift	Groups adjacent pixels that have similar spectral characteristics into segments.
Train Iso Cluster Classifier	Generates an Esri classifier definition file (.ecd) using the Iso Cluster classification definition.
Train K-Nearest Neighbor Classifier	Generates an Esri classifier definition file (.ecd) using the K-Nearest Neighbor classification method.
Train Maximum Likelihood Classifier	Generates an Esri classifier definition file (.ecd) using the Maximum Likelihood Classifier (MLC) classification definition.
Train Random Trees Classifier	Generates an Esri classifier definition file (.ecd) using the Random Trees classification method.
Train Random Trees Regression Model	Models the relationship between explanatory variables and a target dataset using random trees analysis.
Train Support Vector Machine Classifier	Generates an Esri classifier definition file (.ecd) using the Support Vector Machine (SVM) classification definition.
Update Accuracy Assessment Points	Updates the Target field in the attribute table to compare reference points to the classified image.

Deep Learning

Deep Learning tools detect features in imagery by using multiple layers of artificial neural networks where each layer is capable of extracting one or more unique features in the image. The following table lists the available Deep Learning tools and provides a brief description of each.

Tools in the Deep Learning toolset
Tool	Description
Classify Objects Using Deep Learning	Runs a trained deep learning model on an input raster and an optional feature class to produce a feature class or table in which each input object or feature has an assigned class or category label.
Classify Pixels Using Deep Learning	Runs a trained deep learning model on an input raster to produce a classified raster, with each valid pixel having an assigned class label.
Compute Accuracy For Object Detection	Calculates the accuracy of a deep learning model by comparing the detected objects from the Detect Objects Using Deep Learning tool to ground truth data.
Detect Change Using deep Learning	Runs a trained deep learning model to detect change between two rasters.
Detect Objects Using Deep Learning	Runs a trained deep learning model on an input raster to produce a feature class containing the objects it finds. The features can be bounding boxes or polygons around the objects found or points at the centers of the objects.
Export Training Data For Deep Learning	Converts labeled vector or raster data to deep learning training datasets using a remote sensing image. The output is a folder of image chips and a folder of metadata files in the specified format.
Extract Features Using AI Models	Runs one or more pretrained deep learning models on an input raster to extract features and automate the postprocessing of the inferenced outputs.
Non Maximum Suppression	Identifies duplicate features from the output of the Detect Objects Using Deep Learning tool as a postprocessing step and creates a new output with duplicates removed.
Train Deep Learning Model	Trains a deep learning model using the output from the Export Training Data For Deep Learning tool.
Train Using AutoDL	Trains a deep learning model by building training pipelines and automating much of the training process, including data augmentation, model selection, hyperparameter tuning, and batch size deduction.

Extraction

The Extraction toolset allows you to extract a subset of pixels from a raster by the pixels' attributes or their spatial location.

Tools in the Extraction toolset
Tool	Description
Sample	Creates a table or a point feature class that shows the values of cells from a raster, or a set of rasters, for defined locations. The locations are defined by raster cells, points, polylines, or polygons.

Interpolation

The Interpolation toolset allows you to interpolate different types of data.

Tools in the Interpolation toolset
Tool	Description
Interpolate From Spatiotemporal Points	Interpolates temporal point data into a multidimensional raster.
Optimal Interpolation	Statistically assimilates data combined from multiple sources to produce an interpolated output raster.

Map Algebra

Map algebra is a way to perform raster analysis by creating expressions in an algebraic language. Expressions are created with the Raster Calculator tool, which enables you to build expressions that output a raster dataset. The Raster Calculator builds and executes a single map algebra expression using Python syntax.

For more details about the Raster Calculator, refer to An overview of the Map Algebra toolset

Math

More than 60 Math tools are provided for performing mathematical operations on raster datasets. These tools are grouped into functional areas:

General
Conditional
Logical
- Bitwise
- Boolean
- Combinatorial
- Logical
- Relational
Trigonometric

Math (general)

The general Math tools apply a mathematical operation 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 includes 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 following table lists the available general Math tools and provides a brief description of each.

Tools in the general Math toolset
Tool	Description
Abs	Calculates the absolute value of the cells in a raster.
Divide	Divides the values of two rasters on a cell-by-cell basis.
Exp	Calculates the base e exponential of the cells in a raster.
Exp10	Calculates the base 10 exponential of the cells in a raster.
Exp2	Calculates the base 2 exponential of the cells in a raster.
Float	Converts each cell value of a raster into a floating-point representation.
Int	Converts each cell value of a raster to an integer by truncation.
Ln	Calculates the natural logarithm (base e) of cells in a raster.
Log10	Calculates the base 10 logarithm of cells in a raster.
Log2	Calculates the base 2 logarithm of cells in a raster.
Minus	Subtracts the value of the second input raster from the value of the first input raster on a cell-by-cell basis.
Mod	Finds the remainder (modulo) of the first raster when divided by the second raster on a cell-by-cell basis.
Negate	Changes the sign (multiplies by -1) of the cell values of the input raster on a cell-by-cell basis.
Plus	Adds (sums) the values of two rasters on a cell-by-cell basis.
Power	Raises the cell values in a raster to the power of the values found in another raster.
Round Down	Returns the next lower integer value, just represented as a floating point, for each cell in a raster.
Round Up	Returns the next higher integer value, just represented as a floating point, for each cell in a raster.
Square	Calculates the square of the cell values in a raster.
Square Root	Calculates the square root of the cell values in a raster.
Times	Multiplies the values of two rasters on a cell-by-cell basis.

Math (Conditional)

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: either queries on the attributes or a condition based on the position of the conditional statement in a list. The following table lists the available Conditional Math tools and provides a brief description of each.

Tools in the Conditional Math toolset
Tool	Description
Con	Performs a conditional if/else evaluation on each of the input cells of an input raster.
Pick	Uses the value from a position raster to determine from which raster in a list of input raster the output cell value will be obtained.
Set Null	Sets identified cell locations to NoData based on a specified criteria. It returns NoData if a conditional evaluation is true, and returns the value specified by another raster if it is false.

Math (Logical)

The Logical Math tools evaluate the values of the inputs and determine the output values based on Boolean logic. These tools process raster datasets in five main categories: Bitwise, Boolean, Combinatorial, Logical, and Relational. The following tables list the available Logical Math tools and provide a brief description of each.

Bitwise Math tools
Tool	Description
Bitwise And	Performs a Bitwise And operation on the binary values of two input rasters.
Bitwise Left Shift	Performs a Bitwise Left Shift operation on the binary values of two input rasters.
Bitwise Not	Performs a Bitwise Not (complement) operation on the binary value of an input raster.
Bitwise Or	Performs a Bitwise Or operation on the binary values of two input rasters.
Bitwise Right Shift	Performs a Bitwise Right Shift operation on the binary values of two input rasters.
Bitwise XOr	Performs a Bitwise eXclusive Or operation on the binary values of two input rasters.

Boolean Math tools
Tool	Description
Boolean And	Performs a Boolean And operation on the cell values of two input rasters. If both input values are true (non-zero), the output value is 1. If one or both inputs are false (zero), the output is 0.
Boolean Not	Performs a Boolean Not (complement) operation on the cell values of the input raster. If the input values are true (non-zero), the output value is 0. If the input values are false (zero), the output is 1.
Boolean Or	Performs a Boolean Or operation on the cell values of two input rasters. If one or both input values are true (non-zero), the output value is 1. If both input values are false (zero), the output is 0.
Boolean XOr	Performs a Boolean eXclusive Or operation on the cell values of two input rasters. If one input value is true (non-zero) and the other false (zero), the output is 1. If both input values are true or both are false, the output is 0.

Combinatorial Math tools
Tool	Description
Combinatorial And	Performs a Combinatorial And operation on the cell values of two input rasters. If both input values are true (non-zero), the output is a different value for each unique combination of input values. If one or both inputs are false (zero), the output value is 0.
Combinatorial Or	Performs a Combinatorial Or operation on the cell values of two input rasters. If either input value is true (non-zero), the output is a different value for each unique combination of input values. If both inputs are false (zero), the output value is 0.
Combinatorial XOr	Performs a Combinatorial eXclusive Or operation on the cell values of two input rasters. If one input value is true (non-zero) and the other false (zero), the output is a different value for each unique combination of input values. If both inputs are true or both are false, the output value is 0.

Relational Math tools
Tool	Description
Equal To	Performs a Relational equal-to operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster equals the second raster and 0 for cells where it does not.
Greater Than	Performs a Relational greater-than operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster is greater than the second raster and 0 for cells if it is not.
Greater Than Equal	Performs a Relational greater-than-or-equal-to operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster is greater than or equal to the second raster and 0 if it is not.
Less Than	Performs a Relational less-than operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster is less than the second raster and 0 if it is not.
Less Than Equal	Performs a Relational less-than-or-equal-to operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster is less than or equal to the second raster and 0 where it is not.
Not Equal	Performs a Relational not-equal-to operation on two inputs on a cell-by-cell basis. Returns 1 for cells where the first raster is not equal to the second raster and 0 for cells where it is equal.

Logical Math tools
Tool	Description
Diff	Determines which values from the first input are logically different from the values of the second input on a cell-by-cell basis. If the values on the two inputs are different, the value on the first input is output. If the values on the two inputs are the same, the output is 0.
InList	Determines which values from the first input are contained in a set of other inputs, on a cell-by-cell basis. For each cell, if the value of the first input raster is found in any of the list of other inputs, that value will be assigned to the output raster. If it is not found, the output cell will be NoData.
Is Null	Determines which values from the input raster are NoData on a cell-by-cell basis. Returns a value of 1 if the input value is NoData and 0 for cells that are not.
Over	For the cell values in the first input that are not 0, the output value will be that of the first input. Where the cell values are 0, the output will be that of the second input raster.
Test	Performs a Boolean evaluation of the input raster using a logical expression. When the expression evaluates to true, the output cell value is 1. If the expression is false, the output cell value is 0.

Math (Trigonometric)

The Trigonometric Math tools perform various trigonometric calculations on the values in an input raster. The following table lists the available Trigonometric Math tools and provides a brief description of each.

Tools in the Trigonometric Math toolset
Tool	Description
ACos	Calculates the inverse cosine of cells in a raster.
ACosH	Calculates the inverse hyperbolic cosine of cells in a raster.
ASin	Calculates the inverse sine of cells in a raster.
ASinH	Calculates the inverse hyperbolic sine of cells in a raster.
ATan	Calculates the inverse tangent of cells in a raster.
ATan2	Calculates the inverse hyperbolic tangent of cells in a raster.
ATanH	Calculates the inverse hyperbolic tangent of cells in a raster.
Cos	Calculates the cosine of cells in a raster.
CosH	Calculates the hyperbolic cosine of cells in a raster.
Sin	Calculates the sine of cells in a raster.
SinH	Calculates the hyperbolic sine of cells in a raster.
Tan	Calculates the tangent of cells in a raster.
TanH	Calculates the hyperbolic tangent of cells in a raster.

Motion Imagery

The Motion Imagery toolset contains tools for managing, processing, and analyzing motion imagery, including full motion video data. The following table lists the available Motion Imagery tools and provides a brief description of each.

Tools in the Motion Imagery toolset
Tool	Description
Extract Frames To Images	Extracts video frame images and associated metadata from a FMV-compliant video stream and saves the data to a directory.
Video Metadata To Feature Class	Extracts the platform, frame center, frame outline, and attributes metadata from a FMV-compliant video and saves the feature data to a directory.
Video Multiplexer	Creates a video file that combines an archived video stream file and an associated metadata file synchronized by a time stamp.

Multidimensional Analysis

The tools in the Multidimensional Analysis toolset allow you to perform analysis on scientific data across multiple variables and dimensions. The following table lists the available Multidimensional Analysis tools and a brief description of each.

Tools in the Multidimensional Analysis toolset
Tool	Description
Aggregate Multidimensional Raster	Generates a multidimensional raster dataset by combining existing multidimensional raster variables along a dimension.
Dimensional Moving Statistics	Calculates statistics over a moving window on multidimensional data along a specified dimension.
Find Argument Statistics	Extracts the dimension value or band index at which a given statistic is attained for each pixel in a multidimensional or multiband raster.
Generate Multidimensional Anomaly	Computes the anomaly for each slice in an existing multidimensional raster to generate a new multidimensional raster.
Generate Trend Raster	Estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster.
Multidimensional Principal Components	Reduces the number of components that can account for the variance of the whole multidimensional raster so that spatial and temporal patterns can be easily identified.
Predict Using Trend Raster	Computes a forecasted multidimensional raster using the output trend raster from the Generate Trend Raster tool.
Summarize Categorical Raster	Generates a table containing the pixel count for each class, in each slice of an input categorical raster.

Overlay

The tool in the Overlay toolset performs various operations on multiple overlaid rasters. The following table lists the available Overlay tools and provides a brief description of each.

Tools in the Overlay toolset
Tool	Description
Weighted Sum	Overlays several rasters, multiplying each by their given weight and summing them together.

Statistics

Use the Statistics tools to perform statistical raster operations on a local, neighborhood, or zonal basis. The following table lists the tools that perform statistical analysis and provides a brief description of each.

Tools in the Statistics toolset
Tool	Description
Cell Statistics	Calculates a per-cell statistic from multiple rasters. The available statistics are Majority, Maximum, Mean, Median, Minimum, Minority, Percentile, Range, Standard deviation, Sum, and Variety.
Find Argument Statistics	Extract the dimension value (for example the date, height, or depth) at which a specific statistic is reached in the stack of rasters in a multidimensional raster dataset.
Focal Statistics	Calculates for each input cell location a statistic of the values within a specified neighborhood around it.
Zonal Statistics	Summarizes the values of a raster within the zones of another dataset.
Zonal Statistics as Table	Summarizes the values of a raster within the zones of another dataset and reports the results as a table.

Synthetic Aperture Radar

ArcGIS geoprocessing toolset containing tools that correct, process, and enable analysis synthetic aperture radar (SAR) data. The following table lists the available Synthetic Aperture Radar tools and a brief description of each.

Tools in the Synthetic Aperture Radar toolset
Tool	Description
Apply Geometric Terrain Correction	Orthorectifies the input synthetic aperture radar (SAR) data using a range-Doppler backgeocoding algorithm.
Apply Orbit Correction	Updates the orbital information in the synthetic aperture radar (SAR) dataset using a more accurate orbit state vector (OSV) file.
Apply Radiometric Calibration	Converts the input synthetic aperture radar (SAR) reflectivity into physical units of normalized backscatter by normalizing the reflectivity using a reference plane.
Apply Radiometric Terrain Flattening	Corrects the input synthetic aperture radar (SAR) data for radiometric distortions due to topography.
Compute SAR Indices	Computes various SAR indices, such as Radar Vegetation Index (RVI), Radar Forest Degradation Index (RFDI), and Canopy Structure Index (CSI).
Convert SAR Units	Converts the scaling of the input synthetic aperture radar (SAR) data between amplitude and intensity, between linear and decibels (dB), and between complex and intensity.
Create Color Composite	Creates a three-band raster dataset from a multiband raster dataset.
Despeckle	Corrects the input synthetic aperture radar (SAR) data for speckle, which is a result of coherent illumination that resembles a grainy or salt and pepper effect.
Detect Bright Ocean Objects	Detects potential bright human-made objects—such as ships, oil rigs, and windmills—while masking out the synthetic aperture radar (SAR) data outside the region of interest.
Detect Dark Ocean Areas	Identifies potential dark pixels belonging to oil spills or algae, and clusters these pixels, while masking out the synthetic aperture radar (SAR) data outside the region of interest.
Download Orbit File	Downloads the updated orbit files for the input synthetic aperture radar (SAR) data.
Multilook	Averages the input synthetic aperture radar (SAR) data by looks in range and azimuth to approximate square pixels, mitigate despeckle, and reduce SAR tool processing time.
Remove Thermal Noise	Corrects backscatter disturbances caused by thermal noise in the input synthetic aperture radar (SAR) data, resulting in a more seamless image.

Utilities

The Utilities toolset contains tools for preprocessing and postprocessing imagery and derived products,

Tools in the Overlay toolset
Tool	Description
Create Binary Mask	The Utilities toolset contains tools for preprocessing and postprocessing imagery and derived products.