Adaptive Progressive Band Selection for Dimensionality Reduction in Hyperspectral Images

One of the challenging problems in processing high dimensional data, as hyperspectral images, with better spectral and temporal resolution is the computational complexity resulting from processing the huge amount of data volume. Various methods have been developed in the literature for dimensionality reduction, generally divided into two main techniques: data transformation techniques and features selection techniques. The feature selection technique is advantageous compared to transformation techniques in preserving the original data. However, deciding the appropriate number of features to be selected and choosing these features are very challenging since they require exhaustive researches. The progressive feature selection technique is a new concept recently introduced to address these issues based on priority criteria. However, this approach presents limits when these criteria are insufficient or depends on domain applications. In this paper, we present a new approach to improve the Progressive Feature Selection technique by adding new criteria that measure the amount of information present in each band. The endmembers extraction phase of the proposed approach includes both the N-FINDR and the ATGP algorithms. A case based reasoning system is used to choose the optimal criterion for the endmember extraction. The performances of this proposed approach were evaluated using AVIRIS hyperspectral image and the obtained results prove its effectiveness compared to other PBS techniques.