A Novel Spatial-Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery

In recent years, dimensionality reduction (DR) and classification have become important issues of hyperspectral image analysis. In this paper, we propose a new spatial-spectral similarity measure, which maps the distances between two image patches in hyperspectral images. Including spatial information by using the spatial neighbors, the proposed similarity measure is based on the fact that the observed pixels in the images are spatially related, and the meaningful features can be extracted from both the spectral and spatial domains. First, the new similarity measure can effectively exploit the rich spectral and spatial structures of data, thus improving the original k-nearest neighbor (kNN) classification methods. Second, the new similarity measure can be incorporated into existing DR methods including linear or nonlinear techniques. With the merits of the proposed similarity measure, the modified DR methods become effective in dealing with the redundancy resulting from spectral signature as well as the spatial relation among pixels. A comparative study and analysis based on classification experiments using five real hyperspectral data sets, which were acquired by different instruments, is conducted to evaluate the proposed similarity measure. The experimental results demonstrate that the proposed measure is promising for combining spectral and spatial information when applied to DR and classification of hyperspectral data sets.