GBDX

ENVI® Classification Smoothing

This task removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class. The ENVI Classification Smoothing task requires that the image has been pre-processed using the Advanced Image Preprocessor, and that a classification (e.g. the ENVI ISODATAClassification) has been run on the output from pre-processing.

GBDX Registered Name: ENVI_ClassificationSmoothing
Provider: Harris Geospatial Solutions

Imagery Examples

Before: WorldView 2 image after AOP correction

Before: WorldView 2 image after AOP correction

After: WorldView 2 image after smoothing

After: WorldView 2 image after smoothing

Quickstart

This is a workflow example for basic processing.

# Quickstart Example running the task name.

# Initialize the Environment.
from os.path import join, split
from gbdxtools import Interface
gbdx = Interface()

tasks = []
output_location = 'ENVI/ClassificationSmoothing'

cat_id = '105001000672E000'

# Image Auto ordering task parameters
order = gbdx.Task("Auto_Ordering")
order.inputs.cat_id = cat_id
order.impersonation_allowed = True
order.persist = True
order.timeout = 36000
tasks += [order]

# Image AOP task parameters
aop = gbdx.Task("AOP_Strip_Processor")
aop.inputs.data = order.outputs.s3_location.value
aop.inputs.bands = 'MS'
aop.inputs.enable_dra = False
aop.outputs.data.persist = True
aop.outputs.data.persist_location = output_location+'/aop'
aop.timeout = 36000
tasks += [aop]

# Create a basic classification
envi_isodata = gbdx.Task("ENVI_ISODATAClassification")
envi_isodata.inputs.input_raster = aop.outputs.data.value
tasks += [envi_isodata]

# Smooth the classification
envi_smooth = gbdx.Task("ENVI_ClassificationSmoothing")
envi_smooth.inputs.input_raster = envi_isodata.outputs.output_raster_uri.value
envi_smooth.inputs.kernel_size = '9'
tasks += [envi_smooth]

workflow = gbdx.Workflow(tasks)
workflow.savedata(
    envi_smooth.outputs.output_raster_uri, location=output_location
)

workflow.execute()

Inputs

The following table lists all ENVI_ClassificationSmoothing 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 classification raster on which to perform the smooth on. -- 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
kernel_size False 3 string The smooth kernel size, using an odd number (e.g., 3 = 3x3 pixels). -- Value Type: UINT -- Default Value: 3
output_raster_uri_filename False N/A string Specify a string with the fully-qualified path and filename for OUTPUT_RASTER. -- Value Type: STRING

Outputs

The following table lists all ENVI_ClassificationSmoothing 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_raster_uri True N/A directory Output for OUTPUT_RASTER. -- Value Type: ENVIURI

Output structure

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® Classification Smoothing.

Contact

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

ENVI® Classification Smoothing

This task removes speckling noise from a classification image. It uses majority analysis to change spurious pixels within a large single class to that class. The ENVI Classification Smoothing task requires that the image has been pre-processed using the Advanced Image Preprocessor, and that a classification (e.g. the ENVI ISODATAClassification) has been run on the output from pre-processing.

GBDX Registered Name: ENVI_ClassificationSmoothing
Provider: Harris Geospatial Solutions