Nearest neighbor method: Nearest neighbor method directly takes the pixel value closest to a pixel position as the new value of the pixel. The advantage of this method is that it is simple and fast, and it will not change the original grating value, but it will produce a displacement of half a pixel at most.

It is suitable for discrete data representing classification or specific topics, such as land use and vegetation types. The following schematic diagram shows the raster data after geometric transformation of translation and rotation.

It is a process of interpolating the information of one type of pixel according to the information of another type of pixel. In remote sensing, resampling is the process of extracting low-resolution images from high-resolution remote sensing images.

The commonly used resampling methods include nearest neighbor interpolation, bilinear interpolation and cubic convolution interpolation.

Extended data:

Bilinear interpolation method is to calculate the new raster value by weighting the distance from the sampling point to the surrounding four neighboring pixels. The specific operation is to interpolate once in the Y direction (or X direction), and then interpolate once in the X direction (or Y direction), and get the grid value of the pixel through distance weighting calculation.

When resampling with this method, the result is often smoother than that of nearest neighbor method, but it will change the original grid value and lose some local subtle features. It is suitable for continuous data representing a certain phenomenon distribution and terrain surface, such as DEM image, temperature statistics, rainfall distribution, slope and so on. These data are generally continuous surfaces obtained by multiple interpolation of sampling points.

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