Level set segmentation of remotely sensed hyperspectral images

We present a semi-automated supervised hyperspectral image segmentation algorithm based on the level set methodology. In the proposed procedure, seed pixels are automatically selected by their similarity to the training signatures, and speed functions that control the level set propagation are created based on pixel similarity to the seed signature and class discriminator functions. Two sub images from a remotely sensed HYDICE hyperspectral image of the Washington D.C. Mall area in the U.S.A. are used to validate the algorithm. The results of the proposed algorithm are compared to the results using well-known supervised parallepiped or maximum-likelihood classification methods provided in the ERDAS Imagine software suite. The classes are grass, trees, buildings, water, paths and shadows. The results show the efficacy of the new algorithm. The contributions of the paper include: (1) successful application of the level set segmentation methodology to hyperspectral images, and (2) specification of speed functions suitable for controlling the level set propagation.