Diffusion geometric methods for fusion of remotely sensed data

We propose a novel unsupervised learning algorithm that makes use of image fusion to efficiently cluster remote sensing data. Exploiting nonlinear structures in multimodal data, we devise a clustering algorithm based on a random walk in a fused feature space. Constructing the random walk on the fused space enforces that pixels are considered close only if they are close in both sensing modalities. The structure learned by this random walk is combined with density estimation to label all pixels. Spatial information may also be used to regularize the resulting clusterings. We compare the proposed method with several spectral methods for image fusion on both synthetic and real data.