Skip to Main Content
Hyperspectral images are well suited for automatic target detection, but detection performance in shadow is often degraded due to effects such as low signal-to-noise ratio, high dynamic range and spectral distortions. This paper focuses on improving target detection performance for a specific anomaly detector based on a statistical Multinormal Mixture Model (MMM) that is trained on the entire image to produce a global model of the background. It is demonstrated that a simple square root transformation and a hyperspheric transformation may be applied to the radiance image to enhance detection performance. A balancing strategy for the training of the model with respect to light level is shown to be a further improvement.