Study On The Use Of Complexity Measures For Estimation Of Correct Classification Percentage In Hyperspectral Imagery

This study presents image complexity measures applied to hyperspectral images and their relation to the percentage of correct classification (PCC). Specifically, it studies the relationship between these metrics and the PCC for Maximum Likelihood and Angle Detection classifiers. First, many complexity measures were studied to determine if there was a relation between the measure and the PCC. Results showed a correlation of above 0.7 between complexity measures based on entropy and uncertainty and the PCC of the classifiers mentioned above. Once the relation was established, PCC estimators based on the metrics using simple and multiple regression models were designed. This design was performed using data from both synthetic and real images. The real images were from two hyperspectral sensors, the space based AISA and a portable SOC 700 hyperspectral sensor, and include scenes from the Enrique Reef in La Parguera Puerto Rico. The models were then tested with real data. Results show that confidence intervals on the PCC can be reliably obtained for real images.