Exploiting spectral-spatial proximity for classification of hyperspectral data on manifolds

Similarity measures for classification of hyperspectral data in the manifold space are typically based on spectral characteristics. However, samples that are not spectrally separable may cause incorrectly connected graphs and result in noninformative data manifolds. Spatial relationships inherent in remote sensing images can be beneficial for constructing connectivity graphs. A spectral-spatial proximity graph utilizing both spectral characteristics and spatial homogeneity is proposed for robust manifold learning. With the proposed spectral-spatial graph, we are able to extract essential features and preserve important knowledge in a lower dimensional manifold space, where classification tasks can be performed effectively. Two hyperspectral data sets were used to validate the proposed approach. Classification results obtained by the nearest neighbor classifier demonstrate the usefulness of exploiting spectral similarity and spatial proximity for the manifold-based classification.