Rodríguez-Carrión, Nicole M.

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Statistical analysis to determine a spatial resolution to improve image classification
(2015) Rodríguez-Carrión, Nicole M.; Hunt, Shawn D.; College of Engineering; Arzuaga, Emmanuel |Jiménez, Luis O.; Department of Electrical and Computer Engineering; Santiago, Aidsa
This study uses hypothesis testing to determine the optimum pixel size to classify hyperspectral images. Pixel size is defined here as the size of the ground area captured in a pixel. Historically, more resolution or smaller pixel sizes, are considered better, but having smaller pixels can cause difficulties in the image classification. If the pixel size is too small, then the variation in pixels belonging to the same class could be vast. By assuming pixels are identically distributed random variables led to a derivation of a hypothesis test that uses the pixels covariance and variance. This new proposed hypothesis method was compared with results from the parametric hypothesis test F-test, and the non-parametric Ansari-Bradley hypothesis test. Promising similar results for synthetic and real hyperspectral images were obtained, validating the usability of the new proposed hypothesis method within the scope of this study.