Abstract

This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, nBS. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same nBS. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also selected by other BS methods, in this case each band in JBS is prioritized by the frequency of the band appearing in the band subsets to be fused. Such frequency is then used to calculate the priority probability of this particular band in the JBS. Because the JBS is obtained by taking the union of all band subsets, the number of bands in the JBS is at least equal to or greater than nBS. So, there may be more than nBS bands, in which case, BSF uses the frequency-calculated priority probabilities to select nBS bands from JBS. Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose. Of particular interest is that BSF can prioritize bands without band de-correlation, which has been a major issue in many BS methods using band prioritization as a criterion to select bands.

Highlights

  • Hyperspectral imaging has emerged as a promising technique in remote sensing [1] due to its use of hundreds of contiguous spectral bands

  • This type of band selection (BS) method generally relies on band prioritization (BP) criteria [4] specified by data statistics, such as variance, signal-to-noise ratio (SNR), entropy, information divergence (ID), and maximum-information-minimum-redundancy (MIMR) [21], to rank spectral bands, so that bands can be selected according to their ranked orders

  • This is because bands selected by band selection fusion (BSF) can be from different band subsets, which are obtained by various BP criteria or application-based BS methods; 4

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Summary

Introduction

Hyperspectral imaging has emerged as a promising technique in remote sensing [1] due to its use of hundreds of contiguous spectral bands. One is made up of BS methods designed based on data structures and characteristics This type of BS method generally relies on band prioritization (BP) criteria [4] specified by data statistics, such as variance, signal-to-noise ratio (SNR), entropy, information divergence (ID), and maximum-information-minimum-redundancy (MIMR) [21], to rank spectral bands, so that bands can be selected according to their ranked orders. As a result, such BP-based methods are generally unsupervised and completely determined by data itself, not applications, and have two major issues.

Band Selection Fusion
Simultaneous Band Selection Fusion p
Real Hyperspectral Image Experiments
Linear Spectral Unmixing
SBSF Methods
1–3. Last Pixel Found as the Fifth R Panel Pixel
Hyperspectral Image Classification
Discussions
Conclusions
ROSIS Data
Full Text
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