This tool can be used to perform a Gaussian filter on a raster image. A Gaussian filter can be used to emphasize the longer-range variability in an image, effectively acting to smooth the image. This can be useful for reducing the noise in an image. The algorithm operates by convolving a kernel of weights with each grid cell and its neighbours in an image. The weights of the convolution kernel are determined by the 2-dimensional Gaussian (i.e. normal) curve, which gives stronger weighting to cells nearer the kernel centre. It is this characteristic that makes the Gaussian filter an attractive alternative for image smoothing and noise reduction than the Mean filter. The size of the filter is determined by setting the standard deviation parameter; the larger the standard deviation the larger the resulting filter kernel. The standard deviation can be any number in the range 0.5-20.
NoData values in the input image are replaced with the average value of all valid cells within the kernel. This is also the procedure when the neighbourhood around a grid cell extends beyond the edge of the grid. The output raster is of the float data type and continuous data scale.
The following is an example of a Python script that uses this tool:
wd = pluginHost.getWorkingDirectory()
inputFile = wd + "input.dep"
outputFile = wd + "output.dep"
stdDevDist = "0.75"
reflectEdges = "true"
args = [inputFile, outputFile, stdDevDist, reflectEdges]
pluginHost.runPlugin("FilterGaussian", args, False)
This is a Groovy script also using the tool:
def wd = pluginHost.getWorkingDirectory()
def inputFile = wd + "input.dep"
def outputFile = wd + "output.dep"
def stdDevDist = "0.75"
def reflectEdges = "true"
String[] args = [inputFile, outputFile, stdDevDist, reflectEdges]
pluginHost.runPlugin("FilterGaussian", args, false)