Determining optimum pixel size for classification

This work describes a novel method of estimating statistically optimum pixel sizes for classification. Historically more resolution, smaller pixel sizes, are considered better, but having smaller pixels can cause difficulties in classification. If the pixel size is too small, then the variation in pixels belonging to the same class could be very large. This work studies the variance of the pixels for different pixel sizes to try and answer the question of how small, (or how large) can the pixel size be and still have good algorithm performance. Optimum pixel size is defined here as the size when pixels from the same class statistically come from the same distribution. The work first derives ideal results, then compares this to real data. The real hyperspectral data comes from a SOC-700 stand mounted hyperspectral camera. The results compare the theoretical derivations to variances calculated with real data in order to estimate different optimal pixel sizes, and show a good correlation between real and ideal data.