Abstract
In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube. The new selection criterion is denoted as the MRMR selection criterion and the associated BS method is denoted as the MRMR method. The MRMR selection criterion can evaluate the band subset’s representativeness and redundancy simultaneously. For one band subset, its representativeness is estimated by using orthogonal projection (OP) and its redundancy is measured by the average of the Pearson correlation coefficients among the bands in this subset. To find the satisfactory subset, an effective evolutionary algorithm, i.e., the immune clone selection (ICS) algorithm, is applied as the subset searching strategy. Moreover, we further introduce two effective tricks to simplify the computation of the representativeness metric, thus the computational complexity of the proposed method is reduced significantly. Experimental results on different real-world datasets demonstrate that the proposed method is very effective and its selected bands can obtain good classification performances in practice.
Highlights
Hyperspectral images contain large amounts of bands, which brings several problems, such as the heavy computational burden and storage cost
We proposed a novel band selection (BS) selection criterion based on maximizing representativeness and minimizing redundancy, which is denoted as the MRMR selection criterion
In the case of selecting a small number of bands, e.g., less than 12 bands, the accuracy of the MRMR method is about 3% higher than the accuracy of the second best method (i.e., orthogonal-projection-based BS method (OPBS))
Summary
Hyperspectral images contain large amounts of bands, which brings several problems, such as the heavy computational burden and storage cost. The commonly used DR techniques include feature extraction and band selection (i.e., feature selection). Supervised BS methods try to find the most informative bands with respect to the available prior knowledge [8,9], whereas unsupervised methods do not use any object information [10,11]. A vector space is defined as a set that is closed under finite vector addition and scalar multiplication [34]. W is a linear subspace (or a linear manifold) of the vector space spanned by the vector set {x0, x1, x2, · · ·, xm} (note that this set includes x0), so W can be considered as a hyperplane relative to the latter [34]. The orthogonal projection of x0 onto the hyperplane W can be computed by:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
