Using geographically weighted variables for image classification

Abstract

In this study, geographically weighted variables calculated for two tree species, Cryptomeria japonica (Sugi) and Chamaecyparis obtusa (Hinoki), were used in addition to spectral information to classify the two species and one mixed forest class. Spectral values (digital numbers for each band) of ‘Sugi’ and ‘Hinoki’ training samples were used to predict the spectral values for the two species at other locations using the inverse distance weighting (IDW) interpolation method. Next, the similarity between each pixel's spectral values and their IDW predicted values was calculated for both of the tree species. The similarity measures are considered to be geographically weighted because nearer training samples have more of an impact on their calculation. The use of geographically weighted variables resulted in an increase in overall accuracy from 82.2% to 85.9% and an increase in the kappa coefficient from 0.740 to 0.795 for a support vector machine classification.