The Benefits of Context Estimation for Target Spectra Detection in Hyperspectral Imagery

In remotely sensed hyperspectral imagery, many samples are collected on a given flight and many variable factors contribute to the distribution of samples. Various environmental factors transform spectral responses causing them to appear differently in different environmental contexts. Previously, we developed and applied context-based classifiers to hyperspectral imagery, improving classification results. A new variant of our model is presented which incorporates Bayesian classifiers into the model. Classification results are compared to those of a standard Bayesian classifier to identify the direct benefits of context estimation.