4.6.16 Spatial

Basic Description: Spatial applies a high or low pass filter to the chosen area. Low pass filters tend to smooth images, while high pass filters sharpen edges.
Technical Details: Spatial filters use a moving window to calculate the average or a weighted average. For a low-pass filter, the average value replaces the center pixel. For a high pass result, the low-pass result is subtracted. Low pass filtered images "pass" the lower frequencies while filtering out the higher frequencies, smoothing the image. High pass filters subtract the low frequencies and emphasize higher frequencies. High pass filters can help detect edges and boundaries more clearly, but also magnify noise.
Instructions: After choosing a selection method and making your selection on the survey, enter parameters in the following order (go to each section for details):
4.6.16 Spatial


1. Pass
Choose to pass low or high frequencies. Low-pass filters smooth an image, while high pass filters amplify edges and boundaries.


2.  Method
Choose one of three methods for running low and high pass filters:
  1. Gaussian - uses a square or rectangular kernel or window with a gaussian-shaped pattern of weights so that values from pixels farther away from the center are given less weight than those closest to the center pixel.
  2. Average - computes a simple average of the entire convolution window
  3. Disk - Uses a disk-shaped (pillbox) kernel with rounded corners.

Filter Size

3.  Filter Size
Choose the width and height of the search window. A larger window uses more values to compute the statistics.
You can use samples (pixels) or meters to define the filter size. If meters are used, the filter will determine how many pixels to use and round off by the sampling interval. Using meters is particularly useful when you have tiles with different sampling densities in the same survey. In this case the filter size will remain roughly the same across all tiles.


4.  Sigma
This step is only used for gaussian filters. Specify a value greater than zero which defines the shape of the gaussian weight distribution. Higher values give greater weight to pixels farther from the center compared to lower sigma values. The window size should be approximately 2*Sigma +1.
Last Updated June 15, 2011