I'm doing some time-intensive projecting of aerial photos, and I'm curious - what resampling technique is best to use on aerial photos? In ArcMap, my options are NEAREST, BILINEAR, CUBIC, and MAJORITY.
Nearest Neighbor and Majority are recommended for categorical data, whereas Cubic Convolution and Bilinear Interpolation are for continuous data.
I'm curious to know if there's any commonly-used algorithm for projecting aerial photos. I've just finished projecting one image using Nearest Neighbor and it seems to look good, but an aerial photo is not categorical data, so I'm going to try Bilinear next.
EDIT
I wasn't thinking of aerial photos as the same kind of continuous data as DEMs or precipitation data, but whuber pointed out that they are continuous and should be handled as such. Thanks again.
Answer
Aerial photos are continuous data. Each pixel represents the response of a region of a sensor to light directed at it and as that light varies, the response varies continuously. The result is usually discretized (often into 255 or 256) categories, but that doesn't change the nature of the data. Therefore you want to interpolate rather than using categorical algorithms like nearest neighbor or majority. Bilinear interpolation is usually just fine; at some cost in execution time, cubic convolution will retain local contrast a tiny bit better. A small amount of additional blurriness is unavoidable, but that's almost impossible to notice until the image has undergone many such transformations. The errors made with nearest neighbor are much worse in comparison.
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