Correction Process for Non-Systematic Distortions, Resampling Methods, Nearest Neighbour, Advantages, Disadvantages (Especially for GATE-Geospatial 2022)

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Correction Process for Non-Systematic Distortions

  • Locating Ground Control Points: This process employs identification of geographic features on the image called ground control points (GCPs) , whose position is known such as the intersection of streams, highways, airport, runways etc.
  • Longitude and latitude of GCPs can be determined by accurate base maps where maps are lacking GPS is used to determine the Latitude and Longitude from navigation satellites. Thus, a GCP is located in the field and determine its position using GPS. Accurate GCPs are essential to accurate rectification. GCPs should be
    • Reliably matched between source and reference (e. g. coastline features road intersection, etc.)
    • Widely dispersed throughout the source image
Widely Dispersed Throughout the Source

Resampling Methods

  • The location of output pixels derived from the ground control points (GCPs) is used to establish the geometry of the output image and its relationship to the input image. Difference between actual GCP location and their position in the image are used to determine the geometric transformation required to restore the image.
  • This transformation can be done by different resampling methods where original pixels are resampled to match the geometric coordinates. Each resampling method employs a different strategy to estimate values at output grid for given known values for the input grid.
Nearest Neighbour Sampling is the Simplest Method

Nearest neighbour sampling is the simplest method, it preserves original values in the altered scene

Nearest Neighbour

  • The simplest strategy is simply to assign each corrected pixel, the value from the nearest uncorrected pixel.
  • It has the advantages of simplicity and the ability to preserve original values in the altered scene, but it may create noticeable errors, which may be severe in linear features where the realignment of pixels is obvious.


  • Output values are the original input values. Other methods of resampling tend to average surrounding values. This may be an important consideration when discriminating between vegetation types or locating boundaries.
  • Since original data are retained, this method is recommended before classification.
  • Easy to compute and therefore the fastest to use.


  • Produces a choppy, “stair-stepped” effect. The image has a rough appearance relative to the original unrectified data.
  • Data values may be lost, while other values may be duplicated. Figure 7.21 shows an input file (orange) with a yellow output file superimposed. Input values closest to the centre of each output cell are sent to the output file to the right. Notice that values 13 and 22 are lost while values 14 and 24 are duplicate.

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