Despeckle (Image Analyst)

Available with Image Analyst license.

Summary

Corrects the input synthetic aperture radar (SAR) data for speckle, which is a result of coherent illumination that resembles a grainy or salt and pepper effect.

This tool filters out noise while retaining edges and sharp features in the SAR image. The available filters are Lee, Enhanced Lee, Refined Lee, Frost, Kuan, and Gamma MAP.

Usage

• Despeckling SAR imagery improves image interpretation and classification results.

• This tool does not support a geodatabase as an output location.

Parameters

Dialog

Label	Explanation	Data Type
Input Radar Data	The input radar data.	Raster Dataset; Raster Layer
Output Radar Data	The despeckled radar data.	Raster Dataset
Polarization Bands (Optional)	The polarization bands that will be filtered. The first band is selected by default.	String
Filter Type (Optional)	Specifies the type of smoothing algorithm or filter that will be applied. Lee—A spatial filter will be applied to each pixel in an image to reduce the speckle noise. This option filters the data based on local statistics calculated within a square window. This filter is useful for smoothing speckled data that has an additive or multiplicative component. Enhanced Lee—A spatial filter that preserves the sharpness and detail of the image will be applied to reduce the speckle noise. This option is an enhanced version of the Lee filter. This filter is useful for reducing speckle while preserving texture information. Refined Lee—A spatial filter will be applied to selected pixels, based on local statistics, to reduce the speckle noise. This filter uses a nonsquare filter window to match the direction of edges. It is useful for reducing speckle while preserving edges. This is the default. Frost—An exponentially damped circularly symmetric filter that uses local statistics within individual filter windows will be applied to reduce the speckle noise. This does not affect image features at the edges. This filter is useful for reducing speckle while preserving edges. Kuan—A spatial filter will be applied to each pixel in an image to reduce the speckle noise. This filters the data based on local statistics of the centered pixel value that is calculated using the neighboring pixels. This filter is useful for reducing speckle while preserving edges. Gamma MAP—A Bayesian analysis and Gamma distribution filter will be applied to reduce the speckle noise. This filter is useful for reducing speckle while preserving edges.	String
Filter Size (Optional)	Specifies the size of the pixel window that will be used to filter noise. This parameter is only valid when the Filter Type parameter is set to Lee, Enhanced Lee, Frost, Kuan, or Gamma MAP. 3 x 3—A 3-by-3 filter size will be used. This is the default. 5 x 5—A 5-by-5 filter size will be used. 7 x 7—A 7-by-7 filter size will be used. 9 x 9—A 9-by-9 filter size will be used. 11 x 11—An 11-by-11 filter size will be used.	String
Noise Model (Optional)	Specifies the type of noise that is reducing the quality of the radar image. This parameter is only valid when the Filter Type parameter is set to Lee. Multiplicative noise—Random signal noise that is multiplied into the relevant signal during capture or transmission is reducing the quality. This is the default. Additive noise—Random signal noise that is added to the relevant signal during capture or transmission is reducing the quality. Additive and multiplicative noise—A combination of both noise models is reducing the quality.	String
Noise Variance (Optional)	The noise variance of the radar image. The default is 0.25. This parameter is only valid when the Filter Type parameter is set to Lee and the Noise Model parameter is set to Additive noise or Additive and multiplicative noise.	Double
Additive Noise Mean (Optional)	The mean value of additive noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. The default value is 0. This parameter is only valid when the Filter Type parameter is set to Lee and the Noise Model parameter is set to Additive noise or Additive and multiplicative noise.	Double
Multiplicative Noise Mean (Optional)	The mean value of multiplicative noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. The default value is 1. This parameter is only valid when the Filter Type parameter is set to Lee and the Noise Model parameter is set to Multiplicative noise or Additive and multiplicative noise.	Double
Number of Looks (Optional)	The number of looks value of the image, which controls image smoothing and estimates noise variance. A smaller value results in more smoothing, while a larger value retains more image features. The default value is 1. This parameter is only valid when the Filter Type parameter is set to Enhanced Lee, Kuan, or Gamma MAP or when the Filter Type parameter is set to Lee and the Noise Model parameter is set to Multiplicative noise.	Long
Damping Factor (Optional)	The exponential damping level of smoothing that will be applied. A damping value greater than 1 will result in better edge preservation but less smoothing. Values less than 1 will result in more smoothing. A value of 0 will produce results similar to a low-pass filter. The default is 1.	Long

Python

Despeckle(in_radar_data, out_radar_data, {polarization_bands}, {filter_type}, {filter_size}, {noise_model}, {noise_variance}, {add_noise_mean}, {mult_noise_mean}, {number_of_looks}, {damp_factor})

Name	Explanation	Data Type
in_radar_data	The input radar data.	Raster Dataset; Raster Layer
out_radar_data	The despeckled radar data.	Raster Dataset
polarization_bands [polarization_bands,...] (Optional)	The polarization bands that will be filtered. The first band is selected by default.	String
filter_type (Optional)	Specifies the type of smoothing algorithm or filter that will be applied. LEE—A spatial filter will be applied to each pixel in an image to reduce the speckle noise. This option filters the data based on local statistics calculated within a square window. This filter is useful for smoothing speckled data that has an additive or multiplicative component. ENHANCED_LEE—A spatial filter that preserves the sharpness and detail of the image will be applied to reduce the speckle noise. This option is an enhanced version of the Lee filter. This filter is useful for reducing speckle while preserving texture information. REFINED_LEE—A spatial filter will be applied to selected pixels, based on local statistics, to reduce the speckle noise. This filter uses a nonsquare filter window to match the direction of edges. It is useful for reducing speckle while preserving edges. This is the default. FROST—An exponentially damped circularly symmetric filter that uses local statistics within individual filter windows will be applied to reduce the speckle noise. This does not affect image features at the edges. This filter is useful for reducing speckle while preserving edges. KUAN—A spatial filter will be applied to each pixel in an image to reduce the speckle noise. This filters the data based on local statistics of the centered pixel value that is calculated using the neighboring pixels. This filter is useful for reducing speckle while preserving edges. GAMMA_MAP—A Bayesian analysis and Gamma distribution filter will be applied to reduce the speckle noise. This filter is useful for reducing speckle while preserving edges.	String
filter_size (Optional)	Specifies the size of the pixel window that will be used to filter noise. 3x3—A 3-by-3 filter size will be used. This is the default. 5x5—A 5-by-5 filter size will be used. 7x7—A 7-by-7 filter size will be used. 9x9—A 9-by-9 filter size will be used. 11x11—An 11-by-11 filter size will be used. This parameter is only valid when the filter_type parameter is set to LEE, ENHANCED_LEE, FROST, KUAN, or GAMMA_MAP.	String
noise_model (Optional)	This parameter is only valid when the filter_type parameter is set to LEE.	String
noise_variance (Optional)	The noise variance of the radar image. The default is 0.25. This parameter is only valid when the filter_type parameter is set to LEE and the noise_model parameter is set to ADDITIVE_NOISE or ADDITIVE_AND_MULTIPLICATIVE_NOISE.	Double
add_noise_mean (Optional)	The mean value of additive noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. The default value is 0. This parameter is only valid when the filter_type parameter is set to LEE and the noise_model parameter is set to ADDITIVE_NOISE or ADDITIVE_AND_MULTIPLICATIVE_NOISE.	Double
mult_noise_mean (Optional)	The mean value of multiplicative noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. The default value is 1. This parameter is only valid when the filter_type parameter is set to LEE and the noise_model parameter is set to MULTIPLICATIVE_NOISE or ADDITIVE_AND_MULTIPLICATIVE_NOISE.	Double
number_of_looks (Optional)	The number of looks value of the image, which controls image smoothing and estimates noise variance. A smaller value results in more smoothing, while a larger value retains more image features. The default value is 1. This parameter is only valid when the filter_type parameter is set to ENHANCED_LEE, KUAN, or GAMMA_MAP or when the filter_type parameter is set to LEE and the noise_model parameter is set to MULTIPLICATIVE_NOISE.	Long
damp_factor (Optional)	The exponential damping level of smoothing that will be applied. A damping value greater than 1 will result in better edge preservation but less smoothing. Values less than 1 will result in more smoothing. A value of 0 will produce results similar to a low-pass filter. The default is 1.	Long

Code sample

Despeckle example 1 (Python window)

This example despeckles the cross-polarization band using the Refined Lee filter.

import arcpy
arcpy.env.workspace = r"C:\Data\SAR"
outRadar = arcpy.ia.Despeckle("IW_manifest_TNR_CalB0.crf", "VV;VH", "REFINED_LEE")
outRadar.save("IW_manifest_TNR_CalB0_Dspk.crf")

Despeckle example 2 (stand-alone script)

This example despeckles the cross-polarization band using the Refined Lee filter.

# Import system modules and check out ArcGIS Image Analyst extension license
import arcpy
arcpy.CheckOutExtension("ImageAnalyst")
from arcpy.ia import *

# Set local variables
in_radar = r"C:\Data\SAR\manifest_TNR_CalB0.crf"
out_radar = r"C:\Data\SAR\manifest_TNR_CalB0_Dspk.crf" 
polarization = "VV;VH"
filter_type = "REFINED_LEE"

# Execute 
outRadar = arcpy.ia.Despeckle(in_radar, polarization, filter_type) 
outRadar.save(out_radar)

Environments

Cell Alignment, Compression, Current Workspace, NoData, Parallel Processing Factor, Pyramid, Raster Statistics, Resampling Method, Scratch Workspace, Snap Raster, Tile Size