A comparative assessment of space-adaptive analyses of hyperspectral imagery

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In this work, a new strategy for the analysis of hyperspectral data is described and assessed. The image is segmented into spatially homo- geneous areas by means of a three-steps procedure: split the whole hyper- spectral image into homogeneous square blocks of different sizes, merge ad- jacent homogeneous blocks into connected regions, and merge non-adjacent homogeneous blocks into unconnected regions. A reduced data set (RDS) is produced by applying the projection pursuit (PP) algorithm to each of the segments in which the original hyperspectral image has been partitioned based on a spatial homogeneity criterion of pixel spectra. Few significant spectral pixels are extracted from each segment. This operation allows to dramatically reduce the size of the set while maintaining the main informa- tion relative to the whole image. The elements of a basis that repre- sents the RDS are searched for. Algorithms that can be used for this task are whichever methods capable to reduce spectral features: e.g., principal component analysis (PCA) or PP. The best elements representing RDS may constitute a good approximation of the best elements found for the whole hyperspectral image, as the features of RDS are very similar to the features of the original hyperspectral data. Therefore, once the basis has been cal- culated from RDS, it is used to decompose the whole hyperspectral data set. Experiments were carried out on AVIRIS data, for which ground truth was available. Results show that the PCA based on the RDS, even if subopti- mal in the MMSE sense with respect to the conventional PCA, increases the separability of thematic classes, which is favored when pixel vectors in the transformed domain are homogeneously spread around their class centers.