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Classify Pixels Using Deep Learning (Image Analyst)

Mit der Image Analyst-Lizenz verfügbar.

Zusammenfassung

Runs a trained deep learning model on an input raster to produce a classified raster, with each valid pixel having an assigned class label.

The trained deep learning model package consists of an Esri model definition JSON file (.emd). It contains the path to the Python raster function to be called to process each raster tile and the path to the trained binary deep learning model file created from third-party training software such as TensorFlow or PyTorch.

Verwendung

  • You must install the proper deep learning framework Python API (such as TensorFlow or PyTorch) in the ArcGIS Pro Python environment; otherwise, an error will occur when you add the Esri model definition file to the tool. Obtain the appropriate framework information from the creator of the Esri model definition file.

    To set up your machine to use deep learning frameworks in ArcGIS Pro, see Install deep learning frameworks.

  • This tool calls a third-party deep learning Python API (such as PyTorch or Keras) and uses the specified Python raster function to process each object.

  • You can find the sample use cases for this tool on the Esri Python raster function GitHub pageAnatomy of a Python raster function. You can also write custom Python modules by following examples and instructions in the GitHub repository.

  • The Esri model definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string, rather than upload the .emd file. The .dlpk file must be stored locally.

  • For more information about deep learning, see Deep learning in ArcGIS Pro.

  • The following code sample uses the Esri model definition file (.emd):

    {
        "Framework":"TensorFlow",
        "ModelConfiguration":"deeplab",
    
        "ModelFile":"\\Data\\ImgClassification\\TF\\froz_inf_graph.pb",
        "ModelType":"ImageClassification",
        "ExtractBands":[0,1,2],
        "ImageHeight":513,
        "ImageWidth":513,
    
        "Classes" : [
            {
                "Value":0,
                "Name":"Evergreen Forest",
                "Color":[0, 51, 0]
             },
             {
                "Value":1,
                "Name":"Grassland/Herbaceous",
                "Color":[241, 185, 137]
             },
             {
                "Value":2,
                "Name":"Bare Land",
                "Color":[236, 236, 0]
             },
             {
                "Value":3,
                "Name":"Open Water",
                "Color":[0, 0, 117]
             },
             {
                "Value":4,
                "Name":"Scrub/Shrub",
                "Color":[102, 102, 0]
             },
             {
                "Value":5,
                "Name":"Impervious Surface",
                "Color":[236, 236, 236]
             }
        ]
    }

Syntax

ClassifyPixelsUsingDeepLearning(in_raster, in_model_definition, {arguments}, processing_mode)
ParameterErklärungDatentyp
in_raster

The input raster dataset to classify. The input can be a single raster or multiple rasters in a mosaic dataset, an image service, or a folder of images.

Raster Dataset; Raster Layer; Mosaic Layer; Image Service; MapServer; Map Server Layer; Internet Tiled Layer; Folder
in_model_definition

The in_model_definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string, rather than upload the .emd file. The .dlpk file must be stored locally.

It contains the path to the deep learning binary model file, the path to the Python raster function to be used, and other parameters such as preferred tile size or padding.

File; String
arguments
[arguments,...]
(optional)

The function arguments are defined in the Python raster function class. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for adjusting the sensitivity. The names of the arguments are populated by the tool from reading the Python module.

Value Table
processing_mode

Specifies how all raster items in a mosaic dataset or an image service will be processed. This parameter is applied when the input raster is a mosaic dataset or an image service.

  • PROCESS_AS_MOSAICKED_IMAGEAll raster items in the mosaic dataset or image service will be mosaicked together and processed. This is the default.
  • PROCESS_ITEMS_SEPARATELYAll raster items in the mosaic dataset or image service will be processed as separate images.
String

Rückgabewert

NameErklärungDatentyp
out_classified_raster

The classified image.

Each valid pixel has a class value. The raster has an attribute table with class names, values, and colors.

Raster

Codebeispiel

ClassifyPixelsUsingDeepLearning example 1 (Python window)

This example classifies a raster based on a custom pixel classification using the ClassifyPixelsUsingDeepLearning tool.

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

# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")

out_classified_raster = ClassifyPixelsUsingDeepLearning
     ("c:\\classifydata\\moncton_seg.tif", "c:\\classifydata\\moncton_sig.emd", 
     "padding 0; batch_size 16", "PROCESS_AS_MOSAICKED_IMAGE")
Out_classified_raster.save("c:\\classifydata\\classified_moncton.tif")
ClassifyPixelsUsingDeepLearning example 2 (stand-alone script)

This example classifies a raster based on a custom pixel classification using the ClassifyPixelsUsingDeepLearning tool.

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

"""
Usage: ClassifyPixelsUsingDeepLearning(in_raster,out_classified_raster, 
       in_classifier_definition, {arguments}, {processing_mode})
                      
"""

# Set local variables
in_raster = "c:\\classifydata\\moncton_seg.tif"
in_model_definition = "c:\\classifydata\\moncton_sig.emd"
model_arguments = "padding 0; batch_size 16"
processing_mode = "PROCESS_AS_MOSAICKED_IMAGE"

# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")

# Execute 
Out_classified_raster = ClassifyPixelsUsingDeepLearning(in_raster, 
                        in_model_definition, model_arguments, processing_mode)
Out_classified_raster.save("c:\\classifydata\\classified_moncton.tif")

Lizenzinformationen

  • Basic: Erfordert Image Analyst
  • Standard: Erfordert Image Analyst
  • Advanced: Erfordert Image Analyst

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