Hyperspectral Image Classification Based on Nonlocal Means With a Novel Class-Relativity Measurement

Nonlocal means (NLM) algorithm has been proven to be an effective context-sensitive denoising approach, where many similar patches spatially far from a given patch could provide nonlocal constraint to the local structure. For hyperspectral image, however, the conventional NLM algorithm becomes inapplicable for the high number of spectral bands. In this letter, we incorporate the image nonlocal self-similarity into the maximum a posteriori estimation for hyperspectral classification. The main novelty lies in the following two aspects: The NLM algorithm is exploited to combine similar local structures and nonlocal averaging; a new class-relativity measurement is proposed to describe the self-similarity in the context of the hyperspectral classification. Several experiments on simulated and real hyperspectral data sets are provided to demonstrate the effectiveness of the proposed algorithm.