Active Contours with Edges: Combining Hyperspectral and Grayscale Segmentation

In this work, we introduce a method to segment hyperspectral images using a Chan-Vese framework. We utilize a modified l(2) distance especially well-suited for hyperspectral classification problems. This distance considers spectral signal shape rather than illumination for the classification of objects. The practicality of multiple phase segmentation in this application is also demonstrated. We then use a high spatial resolution grayscale or color image and a high spectral, but low spatial resolution hyperspectral image to produced a fused segmentation result that is more accurate than segmentation on either image alone. Lastly, we show that the algorithm also gives a natural method for endmember selection and apply this result to anomaly detection.