Spatial filtering

Spatial filtering is one of the most powerful tool for spatial analysis based on local neighbourhoods. This process involes the use of a moving window, or kernel, of a specified size. The window is moved over-top of each grid cell in a grid and each of the neighbouring cells within the window are examined and some form of mathematical manipulation is performed. Often the manipulation involves convolving the neighbouring values with a specified set of weight values. Spatial filtering is used in many image analysis applications, most notably, smoothing and noise reduction, sharpening, edge detection, line detection, skeletonization and line thinning, and mathematical morphology.

The following spatial filters are available in Whitebox GAT:

Adaptive filter

Diversity

Emboss

Edge-preserving Smoothing (Bilateral)

Gaussian

High-pass

k-Nearest mean

Laplacian

Line detection

Maximum

Mean

Minimum

Median

Mode

Olympic

Percentile

Prewitt

Range

Sobel

Standard deviation

Total

User-defined weights

For further information about the specifics of their algorithms and use, please see their individual help entries.