Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.
Understand and examine the common methods for raster resampling
Explain the impact of the applied resampling method on the quality of the output dataset
Completed (GI-N2K)