Linear Spectral Unmixing (Spatial Analyst Tools)

Summary

Performs subpixel classification and calculates the fractional abundance of different land-cover types for individual pixels.

Linear Spectral Unmixing tool illustration

Usage

This tool calculates the fractional material for individual pixels that contain multiple materials. It generates a multiband raster in which each band corresponds to the fractional abundance of each land-cover class. For example, you can use it to perform land-cover classification on a multispectral image to identify photosynthetic vegetation, bare soil, and dead or nonphotosynthetic vegetation.
This tool supports several forms of spectral data input, such as Esri spectral library (.esl) or ENVI spectral library format (.sli).

The tool supports a legacy form of the JSON spectra input. The following is an example of a spectral file provided as a .json file:

{
  "EsriEndmemberDefinitionFile" : 0,
  "FileVersion" : 1,
  "NumberOfEndmembers" : 3,
  "NumberOfBands" : 7,
  "Endmembers" : [
    {
      "EndmemberID" : 1,
      "EndmemberName" : "urban",
      "SpectralProfile" : [
            88,
            42,
            48,
            38,
            86,
            115,
            59
          ]
    },
    {
      "EndmemberID" : 2,
      "EndmemberName" : "vegetation",
      "SpectralProfile" : [
            50,
            21,
            20,
            35,
            50,
            110,
            23
          ]
    },
    {
      "EndmemberID" : 3,
      "EndmemberName" : "water",
      "SpectralProfile" : [
            51,
            20,
            14,
            9,
            7,
            116,
            4
          ]
    }
  ]
}

Classifier definition files (.ecd) generated from the Train Maximum Likelihood Classifier tool are the only classifier output currently supported.
Polygon features require the following field names:
- classname—A text field indicating the name of the class category
- classvalue—A long integer field containing the integer value for each class category
When calculating the fractional abundance of each land-cover class, the solution may include negative coefficients or fractions. If this occurs, review the training samples in the input spectral profile to confirm that they accurately represent each class. If they appear correct, specify Non-negative for Output Value Option.
The number of bands in the input image and input spectral data must match if the spectral input is an .ecd file, a training sample feature class, or a .json file.
If the input spectra is from a spectral library file (.esl or .sli), spectral resampling will be applied using a band average method if the number of bands do not match. Optionally, you can resample spectra using the Resample Library Spectra tool in the Image Analyst toolbox.
If the spectral data is from a spectral library in which the reflectance values range between 0 and 1, such as the USGS spectral library, the input surface reflectance raster must also contain values between 0 and 1.
If the input image is not within 0-1, such as 8 bit or 16 bit, it is recommend that you collect the material's spectral signature from imagery using the Spectral Viewer tool in the Image Analyst toolbox. Optionally, check the Remove continuum check box to normalize the pixel values and the input spectra library data.
When the Remove continuum check box is checked, a convex hull is fitted over the top of a spectrum using straight-line segments that connect local spectra maxima. The continuum will be removed by dividing it into the actual spectrum for each pixel in the image and the input spectra. This allows comparison of absorption features from a common baseline.

Label	Explanation	Data type
Input Raster	The input surface reflectance raster dataset.	Raster Dataset; Mosaic Dataset; Mosaic Layer; Raster Layer; File; Image Service
Spectra File or Training Features	The spectral information for the different land-cover classes. The file can be any of the following: An Esri spectral library file (`.esl`) created from the Spectral Library browse pane or the Spectral Viewer tool An ENVI spectral library file (`.sli`) A polygon feature class generated from the Training Samples Manager pane An Esri classifier definition file (`.ecd`) that was generated by the Train Maximum Likelihood Classifier tool A `.json` file that contains spectral data	File; Feature Layer; String
Output Value Option (Optional)	Specifies how the output pixel values will be defined. Sum to one—Class values for each pixel will be provided in decimal format with the sum of all classes equal to 1. For example, Class1 = 0.16; Class2 = 0.24; Class3 = 0.60. Non-negative—There will be no negative output values.	String
Remove continuum (Optional)	Specifies whether the spectra will be normalized from an image or reference data. Checked—The spectra will be normalized from an image or reference data. Unchecked—The spectra will not be normalized from an image or reference data. This is the default.	Boolean

Return value

Label	Explanation	Data type
Output Raster	The output multiband raster dataset.	Raster

LinearSpectralUnmixing(in_raster, in_spectral_profile_file, {value_option}, {remove_continuum})

Name	Explanation	Data type
in_raster	The input surface reflectance raster dataset.	Raster Dataset; Mosaic Dataset; Mosaic Layer; Raster Layer; File; Image Service
in_spectral_profile_file	The spectral information for the different land-cover classes. The file can be any of the following: An Esri spectral library file (`.esl`) created from the Spectral Library browse pane or the Spectral Viewer tool An ENVI spectral library file (`.sli`) A polygon feature class generated from the Training Samples Manager pane An Esri classifier definition file (`.ecd`) that was generated by the Train Maximum Likelihood Classifier tool A `.json` file that contains spectral data	File; Feature Layer; String
value_option [value_option,...] (Optional)	Specifies how the output pixel values will be defined. `SUM_TO_ONE`—Class values for each pixel will be provided in decimal format with the sum of all classes equal to 1. For example, Class1 = 0.16; Class2 = 0.24; Class3 = 0.60. `NON_NEGATIVE`—There will be no negative output values.	String
remove_continuum (Optional)	Specifies whether the spectra will be normalized from an image or reference data. `REMOVE_CONTINUUM`—The spectra will be normalized from an image or reference data. `NO_REMOVE_CONTINUUM`—The spectra will not be normalized from an image or reference data. This is the default.	Boolean

Return value

Name	Explanation	Data type
out_raster	The output multiband raster dataset.	Raster

Code sample

LinearSpectralUnmixing example 1 (Python window)

This example calculates the fractional abundance of classes from a classifier definition file (.ecd) and generates a multiband raster.

# Import system modules
import arcpy
from arcpy.sa import *

# Run
unmixing_outputs = LinearSpectralUnmixing("C:/data/landsat7_image.crf",
    "C:/data/train_maxi_likelihood_ecd_output.ecd", "SUM_TO_ONE;NON_NEGATIVE")

# Save output
unmixing_outputs.save("C:/data/unmixing_outputs.crf")

LinearSpectralUnmixing example 2 (stand-alone script)

This example calculates the fractional abundance of classes from a classifier definition file (.ecd) and generates a multiband raster.

# Import system modules
import arcpy
from arcpy.sa import *

# Define input parameters
inFile = "C:/data/landsat7_image.crf"
json_file = "C:/data/customized_endmembers.json"
options = "SUM_TO_ONE"

# Run
unmixing_outputs = LinearSpectralUnmixing(inFile, json_file, options)

# Save output
unmixing_outputs.save("C:/data/unmixing_outputs_using_json.crf")

LinearSpectralUnmixing example 3 (stand-alone script)

This example calculates the fractional abundance of classes from a training sample feature class (.shp) and generates a multiband raster.

# Import system modules
import arcpy
from arcpy.sa import *

# Define input parameters
inFile = "C:/data/landsat7_image.crf"
training_features = "C:/data/training_features.shp"
options = "SUM_TO_ONE;NON_NEGATIVE"

# Run
unmixing_outputs = LinearSpectralUnmixing(inFile, training_features, options)

# Save output
unmixing_outputs.save("C:/data/unmixing_outputs_using_training_features.crf")

Environments

Compression, Current Workspace, Geographic Transformations, NoData, Output CONFIG Keyword, Output Coordinate System, Extent, Parallel Processing Factor, Pyramid, Raster Statistics, Resampling Method, Scratch Workspace, Snap Raster, Tile Size

Licensing information

Basic: Requires Spatial Analyst or Image Analyst
Standard: Requires Spatial Analyst or Image Analyst
Advanced: Requires Spatial Analyst or Image Analyst

Linear Spectral Unmixing (Spatial Analyst Tools)

Summary

Usage

Parameters

Return value

Environments

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