Best-bases feature extraction algorithms for classification of hyperspectral data

Citation
S. Kumar et al., Best-bases feature extraction algorithms for classification of hyperspectral data, IEEE GEOSCI, 39(7), 2001, pp. 1368-1379
Citations number
16
Categorie Soggetti
Eletrical & Eletronics Engineeing
Journal title
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN journal
01962892 → ACNP
Volume
39
Issue
7
Year of publication
2001
Pages
1368 - 1379
Database
ISI
SICI code
0196-2892(200107)39:7<1368:BFEAFC>2.0.ZU;2-#
Abstract
Due to advances in sensor technology, it is now possible to acquire hypersp ectral data simultaneously in hundreds of bands. Algorithms that both reduc e the dimensionality of the data sets and handle highly correlated bands ar e required to exploit the information in these data sets effectively. We pr opose a set of best-bases feature extraction algorithms that are simple, fa st, and highly effective for classification of hyperspectral data. These te chniques intelligently combine subsets of adjacent bands into a smaller num ber of features. Both top-down and bottom-up algorithms are proposed. The t op-down algorithm recursively partitions the bands into two (not necessaril y equal) sets of hands and then replaces each final set of bands by its mea n value. The bottom-up algorithm builds an agglomerative tree by merging hi ghly correlated adjacent bands and projecting them onto their Fisher direct ion, yielding high discrimination among classes. Both these algorithms are used in a pairwise classifier framework where the original C-class problem is divided into a set of ((C)(2)) two-class problems. The new algorithms 1) find variable length bases localized in wavelength, 2 ) favor grouping highly correlated adjacent bands that, when merged either by taking their mean or Fisher linear projection, yield maximum discriminat ion, and 3) seek orthogonal bases for each of the ((C)(2)) two-class proble ms into which a C-class 2 problem can be decomposed. Experiments on an AVIR IS data set for a 12-class problem show significant improvements in classif ication accuracies while using a much smaller number of features. Moreover, the proposed methodology facilitates the extraction of valuable domain kno wledge regarding the importance of certain bands for discriminating specifi c groups of classes.