Hyperspectral boundary detection based on the Busyness Multiple Correlation Edge Detector and Alternating Vector Field Convolution snakes

Abstract

Active contours, or snakes, are broadly used to detect linear features such as edges. However, they are often restricted in the delineation of regions of interest within the hyperspectral domain. In this paper, a new approach is presented, referred to as “Busyness Multiple Correlation Edge Detector”, that enables hyperspectral boundary detection using active contours such as “Alternating Vector Field Convolution” snakes. The combination of “Alternating Vector Field Convolution” snakes with the “Busyness Multiple Correlation Edge Detector” opens a broad set of applications by concurrent high convergence quality and speed. Furthermore, specific snake initialisations are tested. A series of examples are used to both demonstrate the approach and underline the benefits of the new methods.