These resources are brought to you by the Cooperative Extension System and your Local Institution

Articles from our resource area experts.

Remote Sensing Resampling Methods

Last Updated: January 22, 2008 Related resource areas: Geospatial Technology


When remote sensing has been used to create an image, it needs to undergo some form of validation procedure using observational and/or sampling techniques. Failure to do so will reduce the confidence in the final product. The following are examples of remote sensing resampling methods:

Nearest neighbor is a resampling method used in remote sensing. The approach assigns a value to each "corrected" pixel from the nearest "uncorrected" pixel. The advantages of nearest neighbor include simplicity and the ability to preserve original values in the unaltered scene. The disadvantages include noticeable position errors, especially along linear features where the realignment of pixels is obvious.

Nearest Neighbor: This resampling method assigns the digital number (DN) of the closest input pixel (in terms of coordinate location) to the corresponding output pixel.
Nearest Neighbor: This resampling method assigns the digital number (DN) of the closest input pixel (in terms of coordinate location) to the corresponding output pixel.

Bilinear can refer to bilinear filtering or bilinear interpolation. Bilinear filtering is a method used to smooth out when they are displayed larger or smaller than they actually are. Bilinear filtering uses points to perform bilinear interpolation. This is done by interpolating between the four pixels nearest to the point that best represents that pixel (usually in the middle or upper left of the pixel).

Bilinear Interpolation: This resampling method assigns the average digital number (DN) of the four pixels closest to the input pixel (in a 2x2 window) to the corresponding output pixel. The mathematical function is bilinear.
Bilinear Interpolation: This resampling method assigns the average digital number (DN) of the four pixels closest to the input pixel (in a 2x2 window) to the corresponding output pixel. The mathematical function is bilinear.

Cubic convolution is a method used to determine the gray levels in an image. This is determined by the weighted average of the 16 closest pixels to the input coordinates. Then that value is assigned to the output coordinates. This method is slightly better than bilinear interpolation, and it does not have the disjointed appearance of nearest neighbor interpolation. Cubic convolution requires about 10 times the computation time required by the nearest neighbor method.

Cubic Convolution: This resampling method assigns the average DN of the sixteen pixels closest to the input pixel (in a 4x4 window) to the corresponding output pixel. The mathematical function is cubic.
Cubic Convolution: This resampling method assigns the average DN of the sixteen pixels closest to the input pixel (in a 4x4 window) to the corresponding output pixel. The mathematical function is cubic.


Have a specific question? Try asking one of our Experts

Unlike most other resources on the web, we have experts from Universities around the country ready to answer your questions.

Comments

Post a comment about this topic

Please keep comments on topic. To ask a question, please use Ask an Expert. All comments are held for moderation. Comments that include profanity, personal attacks or other inappropriate material will not be posted to the site.

Did you find this page useful?

No one has rated this article yet. Why not be the first?

what is this?
not useful
very useful
 1  2  3  4  5