Distributed raster analytics, based on ArcGIS Image Server, processes raster datasets and remotely sensed imagery with an extensive suite of raster functions. Specified results are automatically stored and published to a distributed raster data store, where they may be shared across your enterprise.

## Robust suite of raster analysis functions

Core to this capability is the suite of more than 200 raster functions provided with ArcGIS. These are available as individual processing functions, or they can be combined into a processing chain as raster function templates (RFT). Raster function templates are custom processing chains that can be tailored for any application, using a variety of input data types and processing functions to facilitate specific workflows.

The raster analysis functions can also be extended by the user with the Python raster function. Custom raster functions can be written in Python and once they are added to the system they can leverage the distributed processing of raster analysis.

Raster functions and RFT's support important distributed processing and storage paradigms, such as on-premises, cloud and web implementations. Both standard and custom raster processing and storage capabilities are elastic, and can be scaled to account for surges in demand, emergencies, shifting priorities and other effects on required capacity, demand and cost. The raster functions support distributed processing to support dynamic processing environments. As the number of processing instances changes, the distribution of raster analysis processes changes to take advantage of processing and storage resources.

These raster functions and RFT-based workflows can be implemented via ArcGIS Pro, ArcGIS REST API, ArcGIS Python API, and JS API's, as well as web map viewer in Enterprise portal. For example, you can use the Generate Raster task to execute distributed raster analysis by giving a JSON object representation of a raster function chain.

## Raster functions and objects available for raster analysis

The table below lists the raster functions available for raster analysis, their descriptions, and associated JSON and Python objects.

Function | Raster Function | Description | Samples | Category |
---|---|---|---|---|

Binary thresholding | Thresholding | The binary Threshold function produces the binary image. It uses the Otsu method and assumes the input image to have a bi-modal histogram. | Analysis | |

Heat index | PythonAdaptor | Calculates apparent temperature based on ambient temperature and relative humidity. | Analysis | |

Kernel density | KernelDensity | Calculates a magnitude-per-unit area from point or polyline features using a kernel function to fit a smoothly tapered surface to each point or polyline. | Analysis | |

NDVI | NDVI | The Normalized Difference Vegetation Index (NDVI) is a standardized index that allows you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster datasetâ€”the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band. For more information, see NDVI function. | Analysis | |

NDVI Colorized | NDVIColorized | Applies the NDVI function on the input image, and then uses a color map or color ramp to display the result. | Analysis | |

Tassel Cap | TasselCap | The Tasseled Cap (Kauth-Thomas) transformation is designed to analyze and map vegetation phenomenology and urban development changes detected by various satellite sensor systems. It is known as the Tasseled Cap transformation due to the shape of the graphical distribution of data. | Analysis | |

Weighted overlay | WeightedOverlay | The WeightedOverlay function allows you to overlay several rasters using a common measurement scale and weights each according to its importance. For more information, see Weighted Overlay function. | Analysis | |

Weight sum | WeightSum | The WeightedSum function allows you to overlay several rasters, multiplying each by their given weight and summing them together. For more information, see Weighted Sum function. | Analysis | |

Wind chill | PythonAdaptor | Wind chill is a way to measure how cold it feels when wind is taken into account. | Analysis | |

Function | Raster Function | Description | Samples | Category |

Contrast and brightness | ContrastBrightness | The ContrastBrightness function enhances the appearance of raster data (imagery) by modifying the brightness or contrast within the image. This function works on 8-bit input raster only. | Appearance | |

Convolution | Convolution | The Convolution function performs filtering on the pixel values in an image, which can be used for sharpening an image, blurring an image, detecting edges within an image, or other kernel-based enhancements. For more information, see Convolution function. | Appearance | |

Line detection horizontal | Convolution | Detects edges along horizontal lines. | Appearance | |

Line detection vertical | Convolution | Detects edges along vertical lines. | Appearance | |

Line detection left diagonal | Convolution | Detects edges along diagonal lines moving from lower right to upper left. | Appearance | |

Line detection right diagonal | Convolution | Detects edges along diagonal lines lower left to upper right. | Appearance | |

Gradient north | Convolution | Edge detection along northern gradients. | Appearance | |

Gradient west | Convolution | Edge detection along western gradients. | Appearance | |

Gradient east | Convolution | Edge detection along eastern gradients. | Appearance | |

Gradient south | Convolution | Edge detection along southern gradients. | Appearance | |

Gradient north-east | Convolution | Edge detection along north-eastern gradients. | Appearance | |

Gradient north-west | Convolution | Edge detection along north-western gradients. | Appearance | |

Smoothing | Convolution | Filters data by reducing local variation and removing noise. The effect is that the high and low values within each neighborhood are averaged out, reducing the extreme values in the data. | Appearance | |

Smoothing 3x3 | Convolution | Filters data by reducing local variation and removing noise. Uses a low-pass 3 by 3 filter to perform the smoothing. | Appearance | |

Smoothing 5x5 | Convolution | Filters data by reducing local variation and removing noise. Uses a low- pass 5 by 5 filter to perform the smoothing. | Appearance | |

Sharpen | Convolution | Accentuates the comparative difference in the values with its neighbors. | Appearance | |

Sharpen More | Convolution | Accentuates the value even more tan the Sharpen operator. | Appearance | |

Sharpening 3x3 | Convolution | A high-pass filter using a 3 by 3 kernel. | Appearance | |

Sharpening 5x5 | Convolution | A high-pass filter using a 5 by 5 kernel. | Appearance | |

Laplacian 3x3 | Convolution | Laplacian filters are often used for edge detection to an image that has first been smoothed to reduce its sensitivity to noise. This uses a 3 by 3 filter. | Appearance | |

Laplacian 5x5 | Convolution | Laplacian filters are often used for edge detection to an image that has first been smoothed to reduce its sensitivity to noise. This uses a 5 by 5 filter. | Appearance | |

Sobel Horizontal | Convolution | Used for horizontal edge detection. | Appearance | |

Sobel Vertical | Convolution | Used for vertical edge detection. | Appearance | |

Point Spread | Convolution | The point spread function portrays the distribution of light from a point source through a lense. This will introduce a slight blurring effect. | Appearance | |

Pansharpening | Pansharpening | The Pansharpening function uses a higher-resolution panchromatic image or raster band to fuse with a lower-resolution, multiband raster dataset to increase the spatial resolution of the multiband image. | Appearance | |

Statistics and Histogram | StatisticsHistogram | The Statistics and Histogram function is used to define the statistics and histogram of a raster. You can insert this function at the end of the function chain to describe the statistics and histogram of a raster function template (RFT). This may be needed to control the default display of the processing result, especially when defining a function chain that contains many functions. | Appearance | |

Stretch (contrast) | Stretch | Calculates the focal statistics for each pixel of an image, base on a defined focal neighborhood. | Appearance | |

Function | Raster Function | Description | Samples | Category |

Classify | Classify | The Classify function classifies a segmented raster to a categorical raster. | Classification | |

Maximum Likelihood Classification | MLClassify | The MLClassify function allows you to perform a supervised classification using the maximum likelihood classification algorithm. The hosting ArcGIS Server needs to have a Spatial Analyst license. | Classification | |

Region grow | Region Grow | The Region Grow function groups neighboring pixels into groups depending on the specified radius from the seed point. The group of pixels or object is assigned a specified fill value. | Classification | |

Segmentation | SegmentMeanShift | The SegmentMeanShift function produces a segmented output. Pixel values in the output image represent the converged RGB colors of the segment. The input raster needs to be a 3-band 8-bit image. If the image service is not a 3-band 8-bit unsigned image, you can use the Stretch function before the SegmentMeanShift function. | Classification | |

Function | Raster Function | Description | Samples | Category |

Color model conversion | Color Model Conversion | Converts the color model of an image from either the hue, saturation, and value (HSV) to red, green, and blue (RGB) or vice versa. | Conversion | |

Colormap | Colormap | The Colormap function transforms the pixel values to display the raster data as a red, green, blue (RGB) color image, based on specific colors in a color map or a color range defined in a color ramp. For more information, see Colormap function. | Conversion | |

Colormap to RGB | Colormap2RGB | Converts a single-band raster with a color map to a three-band (red, green, and blue) raster. | Conversion | |

Complex | Complex | Computes magnitude from complex values. | Conversion | |

Grayscale | Grayscale | Converts a multiband image into a single-band grayscale image. Specified weights can be applied to each of the input bands. | Conversion | |

Rasterize attributes | RasterizeAttributes | The Rasterize Attribute function enriches a raster by adding bands derived from values of specified attributes, from an external table or a feature service. | Conversion | |

Rasterize features | RasterizeFeatures | Convert polygon, polyline and point feature class data to a raster layer. | Conversion | |

Remap | Remap | The Remap function allows you to change or reclassify the pixel values of the raster data. For more information, see Remap function. | Conversion | |

Spectral conversion | SpectralConversion | The Spectral Conversion function applies a matrix to a multiband image to affect the spectral values of the output. This can be used, for example, to convert a false color image to a pseudo color image. | Conversion | |

Unit conversion | UnitConversion | The UnitConversion function performs unit conversions. | Conversion | |

Vector field | VectorField | The VectorField function is used to composite two single-band rasters (each raster represents U/V or Magnitude/Direction) into a two-band raster (each band represents U/V or Magnitude/Direction). Data combination type (U-V or Magnitude-Direction) can also be converted interchangeably with this function. | Conversion | |

Vector field renderer | VectorFieldRenderer | The VectorFieldRenderer function symbolizes a U-V or Magnitude-Direction raster. | Conversion | |

Zonal remap | Zonal remap | This function allows you to remap pixels in a raster based on zones defined in another raster and zone-dependent value mapping defined in a table. | Conversion | |

Function | Raster Function | Description | Samples | Category |

Apparent reflectance | ApparentReflectance | This function adjusts image brightness digital number (DN) values for some satellite sensors. The adjustments are based on sun elevation, acquisition date, and sensor properties to set the gain and bias for each band. | Correction | |

Geometric | Geometric | The Geometric function transforms the image (for example, orthorectification) based on a sensor definition and a terrain model. | Correction | |

Radar calibration | RadarCalibration | Calibration is performed on radar imagery so that the pixel values are a true representation of the radar backscatter. | Correction | |

Sentinel-1 Radiometric Calibration | Sentinel-1 RadiometricCalibration | Performs different types of radiometric calibration on Sentinel-1 data. | Correction | |

Sentinel-1 Thermal Noise Removal | Sentinel-1 Thermal Noise Removal | Removes thermal noise from Sentinel-1 data. | Correction | |

Speckle | Speckle | Filters the speckled radar dataset and smooths out the noise while retaining the edges or sharp features in the image. | Correction | |

Function | Raster Function | Description | Samples | Category |

Attribute table | AttributeTable | Allows you to define an attribute table to symbolize a single-band mosaic dataset or raster dataset. This is useful when you want to present imagery that has discrete categories. | Data Management | |

Buffered | Buffered | The Buffered function is used to optimize the performance of complex function chains. It stores the output in memory of the part of the function chain which comes before it. | Data Management | |

Clip | Clip | Clips a raster using a rectangular shape according to the extents defined or will clip a raster to the shape of an input polygon feature class. The shape defining the clip can clip the extent of the raster or clip out an area within the raster. | Data Management | |

Composite bands | CompositeBand | The CompositeBand function allows you to combine multiple images to form a multiband image. | Data Management | |

Constant | Constant | Creates a virtual raster with a single pixel value that can be used in raster function templates and to process a mosaic dataset. | Data Management | |

Extract bands | ExtractBand | The ExtractBand function allows you to extract one or more bands from a raster, or it can reorder the bands in a multiband image. | Data Management | |

Identity | Identity | This function is used to define the source raster as part of the default mosaicking behavior of the mosaic dataset. This function is a no-op function and takes no arguments except a raster. | Data Management | |

Interpolate irregular data | Interpolate Irregular Data | Some netCDF or HDF datasets store their geolocation as irregularly spaced arrays of pixels or point data. When adding these datasets to a mosaic dataset, the interpolate irregular data function takes the irregularly gridded data and resamples it so each pixel is of uniform size and is square. | Data Management | |

Key metadata | KeyMetadata | This function allows you to insert or override key metadata of a raster. | Data Management | |

Mask | Mask | The Mask function changes the image by specifying a certain pixel value or a range of pixel values as no data. | Data Management | |

Nibble | Nibble | Replaces cells of a raster corresponding to a mask with the values of the nearest neighbors. | Data Management | |

Mosaic rasters | MosaicRasters | Creates a mosaic image out of multiple images. | Data Management | |

Raster information | RasterInfo | Modifies properties of the raster, such as bit depth, NoData value, and cell size. | Data Management | |

Recast | Recast | The Recast function reassigns argument values in an existing function template. | Data Management | |

Reproject | Reproject | The Reproject function modifies the projection of a raster dataset, mosaic dataset, or raster item in a mosaic dataset. It can also resample the data to a new cell size and define an origin. | Data Management | |

Resample | Resample | The Resample function resamples pixel values from a given resolution. | Data Management | |

Swath | Swath | Some netCDF or HDF datasets store their geolocation as irregularly spaced arrays. When adding these datasets to a mosaic dataset, the swath function takes the irregularly gridded data and resamples it so that each pixel is of uniform size and is square. | Data Management | |

Transpose bits | TransposeBits | The TransposeBits function performs a bit operation. It extracts bit values from the source data and assigns them to new bits in the output data. | Data Management | |

Function | Raster Function | Description | Samples | Category |

Cost allocation | CostAllocation | Calculates, for each cell, its least-cost source based on the least accumulative cost over a cost surface. | Distance | |

Cost distance | CostDistance | Calculates the least accumulative cost distance for each cell from or to the least-cost source over a cost surface. | Distance | |

Euclidean allocation | EuclideanAllocation | Calculates, for each cell, the nearest source based on Euclidean distance. | Distance | |

Euclidean distance | EuclideanDistance | Calculates, for each cell, the direction, in degrees, to the nearest source. | Distance | |

Least cost path | Least Cost Path | Calculates the least-cost path from a source to a destination. | Distance | |

Function | Raster Function | Description | Samples | Category |

Fill | Fill | Fills sinks in a surface raster to remove small imperfections in the data. | Hydrology | |

Flow accumulation | FlowAccumulation | Creates a raster of accumulated flow into each cell. A weight factor can optionally be applied. | Hydrology | |

Stream link | StreamLink | Assigns unique values to sections of a raster linear network between intersections. | Hydrology | |

Watershed | Watershed | Determines the contributing area above a set of cells in a raster. | Hydrology | |

Function | Raster Function | Description | Samples | Category |

Absolute value | Abs | Calculates the absolute value of the pixels in a raster. | Math | |

Arithmetic | Arithmetic | The Arithmetic function performs an arithmetic operation between two rasters or a raster and a scalar, and vice versa. | Math | |

Band arithmetic | BandArithmetic | Calculates indexes using predefined formulas or a user-defined expression. | Math | |

GEMI | BandArithmetic | The Global Environmental Monitoring Index (GEMI) is a nonlinear vegetation index for global environmental monitoring from satellite imagery. It's similar to NDVI, but it's less sensitive to atmospheric affects. It is affected by bare soil; therefore, it's not recommended for use in areas of sparse or moderately dense vegetation. | Math | |

GVI | BandArithmetic | The Green Vegetation Index (GVI) was originally designed from Landsat MSS imagery and has been modified for Landsat TM imagery. It's also known as the Landsat TM Tasseled Cap green vegetation index. It could be used with imagery whose bands share the same spectral characteristics. | Math | |

Modified SAVI | BandArithmetic | The Modified Soil Adjusted Vegetation Index (MSAVI2) tries to minimize the effect of bare soil on the SAVI. | Math | |

NDVI | BandArithmetic | The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster datasetâ€”the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band. | Math | |

PVI | BandArithmetic | The Perpendicular Vegetation Index (PVI) is similar to a difference vegetation index; however, it is sensitive to atmospheric variations. When using this method to compare different images, it should only be used on images that have been atmospherically corrected. | Math | |

SAVI | BandArithmetic | The Soil-Adjusted Vegetation Index (SAVI) is a vegetation index that attempts to minimize soil brightness influences using a soil-brightness correction factor. This is often used in arid regions where vegetative cover is low. | Math | |

Sultan's formula | BandArithmetic | The Sultans process takes a six-band 8-bit image and uses the Sultan's formula to produce a three-band 8-bit image. The resulting image highlights rock formations called ophiolites on coastlines. This formula was designed based on the TM or ETM bands of a Landsat 5 or 7 scene. The equations applied to create each output band is as follows:
| Math | |

Transformated SAVI | BandArithmetic | The Transformed Soil Adjusted Vegetation Index (TSAVI) is a vegetation index that attempts to minimize soil brightness influences by assuming the soil line has an arbitrary slope and intercept. | Math | |

Calculator | RasterCalculator | Computes a raster from a raster based mathematical expression. | Math | |

Divide | Local | Divides the values of two rasters on a pixel-by-pixel basis. | Math | |

Exponent | Local | Calculates the base e exponential of the pixels in a raster. | Math | |

Exp10 | Local | Calculates the base 10 exponential of the pixels in a raster. | Math | |

Exp2 | Local | Calculates the base 2 exponential of the pixels in a raster. | Math | |

Float | Local | Converts each pixel value of a raster into a floating-point representation. | Math | |

Integer | Local | Converts each pixel value of a raster to an integer by truncation. | Math | |

Ln | Local | Calculates the natural logarithm (base e) of each pixel in a raster. | Math | |

Log10 | Local | Calculates the base 10 logarithm of each pixel in a raster. | Math | |

Log2 | Local | Calculates the base 2 logarithm of each pixel in a raster. | Math | |

Minus | Local | Subtracts the value of the second input raster from the value of the first input raster on a pixel-by-pixel basis. | Math | |

Modulo | Local | Finds the remainder (modulo) of the first raster when divided by the second raster on a pixel-by-pixel basis. | Math | |

Negate | Local | Changes the sign (multiplies by -1) of the pixel values of the input raster on a pixel-by-pixel basis. | Math | |

Plus | Local | Adds (sums) the values of two rasters on a pixel-by-pixel basis. | Math | |

Power | Local | Raises the pixel values in a raster to the power of the values found in another raster. | Math | |

Round Down | Local | Returns the next lower integer, as a floating point value, for each pixel in a raster. | Math | |

Round Up | Local | Returns the next higher integer, as a floating point value, for each pixel in a raster. | Math | |

Square | Local | Calculates the square of the pixel values in a raster. | Math | |

Square root | Local | Calculates the square root of the pixel values in a raster. | Math | |

Times | Local | Multiplies the values of two rasters on a pixel-by-pixel basis. | Math | |

Function | Raster Function | Description | Samples | Category |

Con | Local | Performs a conditional If, Then, Else operation. When a Con operator is used, there usually needs to be two or more functions chained together, where one function states the criteria and the second function is the Con operator which uses the criteria and dictates what the true and false outputs should be. | Math: Conditional | |

Set Null | Local | 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: Conditional | |

Function | Raster Function | Description | Samples | Category |

Bitwise And | Local | Performs a Bitwise And operation on the binary values of two input rasters. | Math: Logical | |

Bitwise Left Shift | Local | Performs a Bitwise Left Shift operation on the binary values of two input rasters. | Math: Logical | |

Bitwise Not | Local | Performs a Bitwise Not (complement) operation on the binary value of an input raster. | Math: Logical | |

Bitwise Or | Local | Performs a Bitwise Or operation on the binary values of two input rasters. | Math: Logical | |

Bitwise Right Shift | Local | Performs a Bitwise Right Shift operation on the binary values of two input rasters. | Math: Logical | |

Bitwise Xor | Local | Performs a Bitwise eXclusive Or operation on the binary values of two input rasters. | Math: Logical | |

Boolean And | Local | Performs a Boolean And operation on the pixel values of two input rasters. If both input values are true (nonzero), the output value is 1. If one or both input values are false (zero), the output value is 0. | Math: Logical | |

Boolean Not | Local | Performs a Boolean Not (complement) operation on the pixel values of the input raster. | Math: Logical | |

Boolean Or | Local | Performs a Boolean Or operation on the cell values of two input rasters. | Math: Logical | |

Boolean Xor | Local | Performs a Boolean eXclusive Or operation on the cell values of two input rasters. | Math: Logical | |

Equal To | Local | Performs an equal-to operation on two rasters on a pixel-by-pixel basis. | Math: Logical | |

Greater Than | Local | Performs a Relational greater-than operation on two inputs on a pixel-by-pixel basis. | Math: Logical | |

Greater Than Equal | Local | Performs a Relational greater-than-or-equal-to operation on two inputs on a pixel-by-pixel basis. | Math: Logical | |

Is Null | Local | Determines which values from the input raster are NoData on a pixel-by-pixel basis. | Math: Logical | |

Less Than | Local | Performs a Relational less-than operation on two inputs on a pixel-by-pixel basis. | Math: Logical | |

Less Than Equal | Local | Performs a Relational less-than-or-equal-to operation on two inputs on a pixel-by-pixel basis. | Math: Logical | |

Not Equal | Local | Performs a Relational not-equal-to operation on two inputs on a pixel-by-pixel basis. | Math: Logical | |

Function | Raster Function | Description | Samples | Category |

ACos | Local | Calculates the inverse cosine of the pixels in a raster. | Math: Trigonometric | |

ACosH | Local | Calculates the inverse hyperbolic cosine of the pixels in a raster. | Math: Trigonometric | |

ASin | Local | Calculates the inverse sine of the pixels in a raster. | Math: Trigonometric | |

ASinH | Local | Calculates the inverse hyperbolic sine of the pixels in a raster. | Math: Trigonometric | |

ATan | Local | Calculates the inverse tangent of the pixels in a raster. | Math: Trigonometric | |

ATan2 | Local | Calculates the inverse tangent (based on x,y) of the pixels in a raster. | Math: Trigonometric | |

ATanH | Local | Calculates the inverse hyperbolic tangent of the pixels in a raster. | Math: Trigonometric | |

Cos | Local | Calculates the cosine of the pixels in a raster. | Math: Trigonometric | |

CosH | Local | Calculates the hyperbolic cosine of the pixels in a raster. | Math: Trigonometric | |

Sin | Local | Calculates the sine of the pixels in a raster. | Math: Trigonometric | |

SinH | Local | Calculates the hyperbolic sine of the pixels in a raster. | Math: Trigonometric | |

Tan | Local | Calculates the tangent of the pixels in a raster. | Math: Trigonometric | |

TanH | Local | Calculates the hyperbolic tangent of the pixels in a raster. | Math: Trigonometric | |

Function | Raster Function | Description | Samples | Category |

ArgStatistics | ArgStatistics | There are four methods in the ArgStatistics function: ArgMax, ArgMin, ArgMedian, and Duration. | Statistical | |

Arg Max | ArgStatistics | ArgMax stands for the argument of the maximum. In the ArgMax method, all raster bands from every input raster are assigned a 0-based incremental band index. | Statistical | |

Arg Median | ArgStatistics | The ArgMedian method returns the Band index for which the given pixel attains the median value of values from all bands. | Statistical | |

Arg Min | ArgStatistics | ArgMin is the argument of the minimum, which returns the Band index for which the given pixel attains its minimum value. | Statistical | |

Duration | ArgStatistics | The Duration method finds the longest consecutive elements in the array, where each element has a value greater than or equal to minimum and less than or equal to maximum, and then returns its length. | Statistical | |

Cell statistics | CellStatistics | This function calculates statistics from multiple rasters, on a pixel-by-pixel basis. The available statistics are majority, maximum, mean, median, minimum, minority, range, standard deviation, sum, and variety. | Statistical | |

Majority cell statistics | CellStatistics | Determines the value that occurs most often on a pixel-by-pixel basis. | Statistical | |

Maximum cell statistics | Cell Statistics | Determines the largest value on a pixel-by-pixel basis. | Statistical | |

Mean cell statistics | Cell Statistics | Calculates the average on a pixel-by-pixel basis. | Statistical | |

Median cell statistics | Cell Statistics | Calculates the middle value of the pixels on a pixel-by-pixel basis. | Statistical | |

Minimum cell statistics | Cell Statistics | Determines the smallest value on a pixel-by-pixel basis. | Statistical | |

Minority cell statistics | Cell Statistics | Determines the value that occurs least often on a pixel-by-pixel basis. | Statistical | |

Range cell statistics | Cell Statistics | Calculates the difference between the largest and the smallest value on a pixel-by-pixel basis. | Statistical | |

Standard Deviation cell statistics | Cell Statistics | Calculates the standard deviation of the pixels on a pixel-by-pixel basis. | Statistical | |

Sum cell statistics | Cell Statistics | Calculates the total value on a pixel-by-pixel basis. | Statistical | |

Variety cell statistics | Cell Statistics | Calculates the number of unique values on a pixel-by-pixel basis. | Statistical | |

Statistics | Statistics | The Statistics function calculates focal statistics for each pixel of an image based on a defined focal neighborhood. | Statistical | |

Zonal statistics | ZonalStatistics | Calculates statistics on values of a raster within the zones of another dataset. | Statistical | |

Function | Raster Function | Description | Samples | Category |

Aspect | Aspect | Aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. Aspect can be thought of as the slope direction. The values of the output raster will be the compass direction of the aspect. | Surface | |

Contour | Contour | The Contour function generates contour lines by joining points with the same elevation from a raster elevation dataset. The contours are isolines created as rasters for visualization. | Surface | |

Curvature | Curvature | The Curvature function displays the shape or curvature of the slope. A part of a surface can be concave or convex; you can tell that by looking at the curvature value. The curvature is calculated by computing the second derivative of the surface. | Surface | |

Elevation void fill | ElevationVoidFill | The Elevation Void Fill function is used to create pixels where holes exist in your elevation. | Surface | |

Hillshade | Hillshade | The hillshade function produces a grayscale 3D representation of the terrain surface, with the sun's relative position taken into account for shading the image. | Surface | |

Shaded relief | ShadedRelief | A color 3D representation of the terrain is created by merging the images from the elevation-coded and hillshade methods. This function uses the altitude and azimuth properties to specify the sun's position. | Surface | |

Slope | Slope | Slope represents the rate of change of elevation for each digital elevation model (DEM) cell. It's the first derivative of a DEM. | Surface | |

Viewshed | Viewshed | Determines the raster surface locations visible to a set of observer features using geodesic methods | Surface |