Band Priority Index: A Feature Selection Framework for Hyperspectral Imagery

Abstract

Hyperspectral Band Selection (BS) aims to select a few informative and distinctive bands to represent the whole image cube. In this paper, an unsupervised BS framework named the band priority index (BPI) is proposed. The basic idea of BPI is to find the bands with large amounts of information and low correlation. Sequential forward search (SFS) is used to avoid an exhaustive search, and the objective function of BPI consist of two parts: the information metric and the correlation metric. We proposed a new band correlation metric, namely, the joint correlation coefficient (JCC), to estimate the joint correlation between a single band and multiple bands. JCC uses the angle between a band and the hyperplane determined by a band set to evaluate the correlation between them. To estimate the amount of information, the variance and entropy are used as the information metric for BPI, respectively. Since BPI is a framework for BS, other information metrics and different mathematic functions of the angle can also be used in the model, which means there are various implementations of BPI. The BPI-based methods have the advantages as follows: (1) The selected bands are informative and distinctive. (2) The BPI-based methods usually have good computational efficiencies. (3) These methods have the potential to determine the number of bands to be selected. The experimental results on different real hyperspectral datasets demonstrate that the BPI-based methods are highly efficient and accurate BS methods.

Low-Cost Multispectral System Design for Pigment Analysis in Works of Art

“…More recently, such studies have been used to inform band selections for MSI remote sensing applications. After testing several band selection methods, the band priority index method (BPI) [ 13 ] was chosen due to its performance on benchmark HSI datasets for remote sensing: “Indian Pines” and “Pavia University” [ 14 ]. The BPI method aims to identify the spectral bands from the HSI dataset with the largest amount of information and the least correlation.…”

“…The objective function (score) of BPI is the product of the joint correlation and the variance in the band being considered for inclusion. As described by [ 13 ], the score calculated for each selected band decreases as more bands are added. If the score of a newly selected band is similar to the previous score, the new band offers little additional contribution, indicating that the number of useful bands has been determined.…”

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

“…Choosing an optimal single band or band pair through methods such as principle component analysis (PCA) [16], analysis of variance (ANOVA) [17], correlation analysis [18], and beta coefficient of partial least squares regression analyses [19] is well established for a detecting differences within and among samples. In addition, sequential forward selection (SFS) is the preferred method for finding an optimal combination of wavelengths since it chooses a subset of wavelengths without losing or deforming the data [20]. For example, Haiyan Cen et al (2016) [21], used SFS methods as one feature selection method for reducing the dimension of hyperspectral imaging data.…”

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

“…From another perspective, the existing DR methods can also be classified into two types: feature extraction [5][6][7] and feature selection [8,9]. When feature selection is concerned, it refers to select feature subset which can retain the most original information of hyperspectral images according to some designed criteria.…”