Mutual Information-Based Hierarchical Band Selection Approach for Hyperspectral Images

Hyperspectral images consist of hundreds of spectral bands with relatively narrow bandwidth and hence records detailed information of the objects. Due to this detailed and enormous amount of information content, the use of hyperspectral images has become very popular in various fields such as land cover monitoring, agriculture, defense, etc. However, this increased spectral dimension results in increased computational complexity. Hence, the selection of minimal subset of spectral bands to represent the actual information effectively without much degradation is a challenge in the field of hyperspectral image analysis. This paper proposes a hierarchical band selection approach by constructing a spectral partition tree-based on mutual information. Initially, each spectral band has been considered as a leaf node. To minimize the redundancy of information carried by neighboring bands, in every level, new nodes are created by merging adjacent bands or group of bands, for which mutual information has been used as the deciding criterion. Finally from each group of bands, a representative band is selected which jointly form the set of selected bands. Experiment is carried out on the AVIRIS Indian Pines dataset by designing training and testing samples containing only the selected set of bands. The experimental results of the proposed method are found to be very promising and competitive with the existing techniques.