Which resampling technique interpolates values from 16 surrounding cells?

The resampling technique that utilizes values from 16 surrounding cells is cubic interpolation. This method is a more advanced technique that calculates an interpolated value based on the nearest 16 cells in a 4x4 grid surrounding the target cell.

Cubic interpolation works by considering the values of these surrounding cells to produce a smoother and more accurate output than simpler methods, like nearest neighbor and bilinear interpolation. By using cubic functions, it can produce curves that more accurately represent the underlying data's trends, which is particularly beneficial when handling continuous data, such as elevation or temperature.

In contrast, nearest neighbor interpolation chooses the nearest cell value without considering any surrounding cells, and bilinear interpolation only uses the nearest four cells (forming a 2x2 square grid), resulting in a less smooth output compared to cubic interpolation. The majority technique is primarily used in categorical data classification and does not perform interpolation in the same way as the other methods. Thus, cubic interpolation effectively captures the variations in the dataset by leveraging more surrounding data points, resulting in a higher fidelity representation of the attribute being analyzed.