Speckle

Summary

Creates a raster object by removing speckle and smoothing out noise in radar datasets, while retaining edges and sharp features in the image.

Discussion

For more information about how this function works, see the Speckle raster function.

The referenced raster dataset for the raster object is temporary. To make it permanent, you can call the raster object's save method.

Syntax

Speckle(raster, {filter_type}, {filter_size}, {noise_model}, {noise_var}, {additive_noise_mean}, {multiplicative_noise_mean}, {nlooks}, {damp_factor})

Parameter	Explanation	Data Type
raster	The input raster.	Raster
filter_type	Specifies the filter type that will be used in the smoothing algorithm to remove speckle noise. Lee—The Lee filter will be used, which reduces the speckle noise by applying a spatial filter to each pixel in an image and filters the data based on local statistics calculated within a square window. EnhancedLee—The EnhancedLee filter will be used, which is a refined version of the Lee filter that reduces the speckle noise effectively by preserving image sharpness and detail. This filter requires the `damp_factor` and `nlooks` arguments to be set. Frost—The Frost filter will be used, which reduces speckle noise and preserves important image features at the edges with an exponentially damped circularly symmetric filter that uses local statistics within individual filter windows. This filter requires the `damp_factor` argument to be set. Kuan—The Kuan filter will be used, which follows a similar filtering process to the Lee filter in reducing speckle noise. This filter also applies a spatial filter to each pixel in an image, filtering the data based on local statistics of the centered pixel value that is calculated using the neighboring pixels. The Kuan filter requires the `nlooks` argument to be set, which controls image smoothing and estimates noise variance. Gamma MAP—A Bayesian analysis and gamma distribution filter will be used to reduce the speckle noise. This filter is useful for reducing speckle while preserving edges. 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. The default value is Lee.	String
filter_size	Specifies the size of the filter window in pixels. 3x3—The pixel size of the filter window is 3 by 3. 5x5—The pixel size of the filter window is 5 by 5. 7x7—The pixel size of the filter window is 7 by 7. 9x9—The pixel size of the filter window is 9 by 9. 11x11—The pixel size of the filter window is 11 by 11. The default value is 3x3.	String
noise_model	Specifies the type of noise that is reducing the quality of the radar image. This argument is only valid when the `filter_type` argument is set to `Lee`. Multiplicative—Random signal noise that is multiplied into the relevant signal during capture or transmission is reducing the quality of the image. Additive—Random signal noise that is added into the relevant signal during capture or transmission is reducing the quality of the image. AdditiveAndMultiplicative—A combination of both noise models is reducing the quality of the image. The default value is Multiplicative.	String
noise_var	The noise variance of the radar image. This argument is only valid when the `filter_type` argument is set to `Lee` and the `noise_model` argument is set to `Additive` or `AdditiveAndMultiplicative`. The default value is None.	Double
additive_noise_mean	The mean value of additive noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. This argument is only valid when the `filter_type` argument is set to `Lee` and the `noise_model` argument is set to `Additive` or `AdditiveAndMultiplicative`. The default value is None.	Double
multiplicative_noise_mean	The mean value of multiplicative noise. A larger noise mean value will produce less smoothing, while a smaller value results in more smoothing. This argument is only valid when the `filter_type` argument is set to `Lee` and the `noise_model` argument is set to `Multiplicative` or `AdditiveAndMultiplicative`. The default value is 1.	Double
nlooks	The number of looks 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. This argument is only valid when the `filter_type` argument is set to `Lee` and the `noise_model` argument is set to `Multiplicative`, or when the `filter_type` argument is set to `Kuan`. The default value is 1.	Integer
damp_factor	The extent of exponential damping effect on filtering. A larger damping value preserves edges better but smooths less, while a smaller value produces more smoothing. This parameter is only valid when the `filter_type` argument is set to `EnhancedLee` or `Frost`. The default value is None.	Double

Return value

Data Type	Explanation
Raster	The output raster.

Code sample

Speckle example

Applies the Lee filter to Sentinel 1 radar imagery.

import arcpy

out_speckle_raster = arcpy.sa.Speckle("Sentinel_1.tif", filter_type="Lee", filter_size="3x3",
                                              noise_model="AdditiveAndMultiplicative", noise_var=0.25,
                                              additive_noise_mean=0, multiplicative_noise_mean=1)