Roving Windows: Filters (Especially for GATE-Geospatial 2022)

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Neighborhoods may be defined in terms of a unifying attributes for an entire area (total analysis of neighborhood) or the focus may be on smaller portions of the total area (a targeted analysis/immediate neighborhood) .


A matrix of numbers used to modify grid cell/pixel values of original data using mathematical procedures. Filter types are:

Diagram Shows Types of Filters

Some functions use a specified window to reclassify cells for a calculation of edginess. Such windowing functions are called filters. In roving window, the window contains numbers against which the grid cells are to be compared.

This technique is more used in remote sensing

  • To isolate edges (high pass filter) ,
  • To emphasize trends by eliminating small pockets of unusual values (low pass filter)
  • To give measure of orientation (directional filter) .

High-Pass Filter

High pass filters are used to emphasize high spatial frequency data. Often they are used to enhance and sharpen features such as roads, land water boundaries. These filters are often referred to as sharpening filters because they generally enhance edges without affecting the low frequency portions of the image. A high pass filter tends to retain the high frequency information within an image while reducing the low frequency information. The kernel of the high pass filter is designed to increase the brightness of the center pixel relative to neighborhood pixels. The kernel array usually contains a single positive value at its center, which is completely surrounded by negative values.

Example Shows High-Pass Filters

The standard method for performing a high-pass filter is to create filter with the weighting value 9 at the center grid cell that is (2,2) and weight of -1 for rest of grid cell. This filter is placed over each matrix of grid cells and members of each corresponding grid cell pair are multiplied.

Now, the next newly created values are summed to obtain the final high-pass value for center grid cell. The next step is to more the move the filter one grid cell to the right, so the central grid cell is now (3,2) .

Repeated the same calculation, the procedure is repeated for entire coverage. With the result, all the lower values are suppressed; all higher values are enhanced or made larger.

Low-Pass Filter

A low pass filter is the basis for most smoothing methods. Low pass filters are used to emphasize low spatial frequency data and are designed to smooth out an image with high frequency. Using a low pass filter tends to retain the low frequency information within an image while reducing the high frequency information.

Example Shows Low-Pass Filter


  • The normal method of filtering in raster database employs the 3 × 3 filter.
  • There is no need to stick to this standard.
  • Most software is giving flexibility to select filter size.
  • Smaller filters are more often used for edge enhancement, whereas large filters are used as low-pass filter.

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