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Comparison of Image Resampling Techniques for Satellite Imagery

Image resampling is a process used to interpolate the new cell values of a raster image during a resizing operation. There are many resampling methods available, through a variety of platforms, including GIS and image-editing software. Each resampling method has strengths and weaknesses which should be considered carefully. The purpose of this paper was not to find a “best” method, but rather to explore how different methods implemented by different software vendors (in this case, ArcGIS and Paint Shop Pro) compared. Aggregated Average and Nearest Neighbor are two commonly used resampling methods, but numerous other methods are available (e.g., bicubic, bilinear, cubic convolution, pixel resize, and weighted average). To compare these methods, Landsat imagery was iteratively resampled from 28.5 to 100 meters per pixel (mpp) using each of the available methodologies. Correlation coefficients were determined comparing each resampled image against imagery resampled using 1) aggregated average and 2) nearest neighbor. Resulting correlation coefficients (R) ranged from 0.98 to 0.34. Correlation between aggregated average and nearest neighbor was relatively low (R = 0.41) as was the correlation between two bilinear interpolation results (R = 0.393) as implemented by different software programs. It was concluded that resampling methods should be considered carefully and tested before selecting a software program or technique, as different programs can implement the same method very differently.