On distances, paths and connections for hyperspectral image segmentation
This work addresses segmentation challenges in hyperspectral imaging, offering incremental improvements by enhancing existing methods with regional controls.
The paper tackles the problem of hyperspectral image segmentation by introducing $\\eta$ and $\\eta$ connections to incorporate regional information into $\\lambda$-flat zones, which are initially based on local data, resulting in a finer segmentation through $\\eta$-bounded regions and $\\mu$-geodesic balls.
The present paper introduces the $η$ and η connections in order to add regional information on $λ$-flat zones, which only take into account a local information. A top-down approach is considered. First $λ$-flat zones are built in a way leading to a sub-segmentation. Then a finer segmentation is obtained by computing $η$-bounded regions and $μ$-geodesic balls inside the $λ$-flat zones. The proposed algorithms for the construction of new partitions are based on queues with an ordered selection of seeds using the cumulative distance. $η$-bounded regions offers a control on the variations of amplitude in the class from a point, called center, and $μ$-geodesic balls controls the "size" of the class. These results are applied to hyperspectral images.