Final answer:
The statement is false. Resampling a raster dataset often changes the cell size and assigns new values based on algorithms like nearest neighbor or bilinear interpolation.
Step-by-step explanation:
The statement is false; resampling a raster dataset can involve changing the cell size and assigning new values to the raster data. Resampling is the process of interpolating the values of a raster to change its spatial resolution, which can mean either increasing or decreasing the cell size. In resampling, algorithms such as nearest neighbor, bilinear interpolation, or cubic convolution are typically used to calculate the new cell values based on the original data. When the cell size is altered, the number of cells in the resulting raster usually changes, and therefore the representation of the data is modified.
If the resampling function is applied to a raster data, it can result in changing the cell size as well as assigning new values to the cells. The purpose of resampling is to alter the resolution or size of the raster data to match the desired specifications. Depending on the resampling method used, such as nearest-neighbor, bilinear, or cubic convolution, the cell size can be adjusted during the resampling process.
For example, when resampling a satellite image with a high resolution to a lower resolution, the cell size will be adjusted, effectively reducing the amount of information contained within each cell. This can lead to loss of detail and precision.