Resampling Methods

Resampling Methods

If a product is projected, the pixel centers of the target product generally not correspond to the centers of the pixels of the input product. Resampling entitles the process of determination and interpolation of pixels in the source product for computation of the pixel values in the target product. The effects of resampling will especially be visible if the pixels in the target product are larger than the source pixels.
SNAP provides three different resampling methods for this computation.

Nearest Neighbour

Every pixel value in the output product is set to the nearest input pixel value.

Pros	Cons
Very simple, fast	Some pixels get lost and others are duplicated
No new values are calculated by interpolation	Loss of sharpness
Fast, compared to Cubic Convolution resampling

Following figure demonstrates the calculation of the new pixel value.

Bi-linear Interpolation

Calculation of the new pixel value is performed by the weight of the four surrounding pixels.

Pros	Cons
Extremas are balanced	Less contrast compared to Nearest Neighbour
Image losses sharpness compared to Nearest Neighbour	New values are calculated which are not present in the input product

Following figure demonstrates the calculation of the new pixel value.

Cubic Convolution

Calculation of the new pixel value is performed by weighting the 16 surrounding pixels.

Pros	Cons
Extremas are balanced	Less contrast compared to Nearest Neighbour
Image is sharper compared to Bi-linear Interpolation	New values are calculated which are not present in the input product
	Slow, compared to Nearest Neighbour resampling

Following figure demonstrates the calculation of the new pixel value.

Visual Comparison of the Resampling Methods

Nearest Neighbour	Bi-linear Interpolation	Cubic Convolution