Object segmentation in hyperspectral images using active contours and graph cuts

The interest in object segmentation on hyperspectral images is increasing and many approaches have been proposed to deal with this area. In this project, we developed an algorithm that combines both the active contours and the graph cut approaches for object segmentation in hyperspectral images. The active contours approach has the advantage of producing subregions with continuous boundaries. The graph cut approach has emerged as a technique for minimizing energy functions while avoiding the problems of local minima. Additionally, it guarantees continuity and produces smooth contours, free of self-crossing and uneven spacing problems. The algorithm uses the complete spectral signature of a pixel and also considers spatial neighbourhood for graph construction, thereby combining both spectral and spatial information present in the image. The algorithm is tested using real hyperspectral images taken from a variety of sensors, such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Data Imagery Collection Experiment (HYDICE), and also taken by the SOC hyperspectral camera. This approach can segment different objects in an image. This algorithm can be applied in many fields and it should represent an important advance in the field of object segmentation.