GBDX

ENVI® Mahalanobis Distance Classification

This task performs a Mahalanobis Distance supervised classification. Mahalanobis Distance is a direction-sensitive distance classifier that uses statistics for each class. It is similar to Maximum Likelihood classification, but it assumes all class covariances are equal and therefore is a faster method. All pixels are classified to the closest training data.

GBDX Registered Name: ENVI_MahalanobisDistanceClassification
Provider: Harris Geospatial Solutions

Inputs

The following table lists all ENVI_MahalanobisDistanceClassification inputs.
Mandatory (optional) settings are listed as Required = True (Required = False).

Name Required Default Valid Values Description
file_types False N/A string GBDX Option. Comma seperated list of permitted file type extensions. Use this to filter input files -- Value Type: STRING[*]
input_raster True N/A See ENVIRASTER input type Specify a raster on which to perform supervised classification. -- Value Type: ENVIRASTER
input_raster_format False N/A See ENVIRASTER input type Provide the format of the image, for example: landsat-8. -- Value Type: STRING
input_raster_band_grouping False N/A See ENVIRASTER input type Provide the name of the band grouping to be used in the task, ie - panchromatic. -- Value Type: STRING
input_raster_filename False N/A See ENVIRASTER input type Provide the explicit relative raster filename that ENVI will open. This overrides any file lookup in the task runner. -- Value Type: STRING
threshold_max_distance False N/A string Specify a pixel value between 0 and 10000000 that applies to all classes, or specify an array of pixel values, one for each class. The number of array elements must equal the number of classes. Mahalanobis Distance accounts for possible non-spherical probability distributions. This value represents the distance within which a class must fall from the center or mean of the distribution for a class. The smaller the distance threshold, the more pixels that are unclassified. -- Value Type: FLOATARRAY
class_colors False N/A string This is an array of RGB triplets representing the class colors as defined by the input vector. -- Value Type: BYTEARRAY
covariance True N/A string Specify an array that is [number of bands, number of bands,number of classes] -- Value Type: DOUBLEARRAY
output_rule_raster_uri_filename False N/A string Specify a string with the fully-qualified path and filename for OUTPUT_RULE_RASTER. -- Value Type: STRING
output_raster_uri_filename False N/A string Specify a string with the fully-qualified path and filename for OUTPUT_RASTER. -- Value Type: STRING
class_pixel_count True N/A string Specify an array that is the number of pixels per class [number of classes]. -- Value Type: LONGARRAY
class_names False N/A string This is an array of class names as defined by the input vector. -- Value Type: STRINGARRAY
mean True N/A string Specify an array that is [number of bands,number of classes] -- Value Type: DOUBLEARRAY

Outputs

The following table lists all ENVI_MahalanobisDistanceClassification outputs.
Mandatory (optional) settings are listed as Required = True (Required = False).

Name Required Default Valid Values Description
task_meta_data False N/A directory GBDX Option. Output location for task meta data such as execution log and output JSON
output_rule_raster_uri True N/A directory Output for OUTPUT_RULE_RASTER. -- Value Type: ENVIURI
output_raster_uri True N/A directory Output for OUTPUT_RASTER. -- Value Type: ENVIURI

Output structure

The output_rule_raster_uri file will be written to the specified S3 Customer Account Location.The output_raster_uri file will be written to the specified S3 Customer Account Location.

Background

For additional background information on this task please refer to the Harris Geospatial ENVI documentation and
ENVI® Mahalanobis Distance Classification.

Contact

If you have any questions or issues with this task, please contact gbdx-support@digitalglobe.com.

ENVI® Mahalanobis Distance Classification

This task performs a Mahalanobis Distance supervised classification. Mahalanobis Distance is a direction-sensitive distance classifier that uses statistics for each class. It is similar to Maximum Likelihood classification, but it assumes all class covariances are equal and therefore is a faster method. All pixels are classified to the closest training data.

GBDX Registered Name: ENVI_MahalanobisDistanceClassification
Provider: Harris Geospatial Solutions