Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection

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.

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

“…As the combination of filter and wrapper models, hybrid models combine the benefits of both and avoid their weaknesses, thus promising better results. Many recent studies [15][16][17][18] have applied hybrid models for FS on HSIs. For instance, Xie [15] divided the spectral interval by the filter method of information gain and then combined the GWO algorithm with the SVM classifier to form a wrapper model to obtain the best feature subset; Wang [18] first used the correlation coefficient metric to cull the highly relevant bands and then used the wrapper model containing Sine cosine algorithm (SCA) algorithm to perform a refined search.…”

“…In particular, many studies [10,[23][24][25][26] have addressed the HSI classification problem by using SVM and obtained superior classification accuracy. Since the FS problem is an NP-hard problem [16], combining an exhaustive search with a classifier is impractical to evaluate all feature subsets except for small-sized feature spaces. Therefore, most wrapper methods are suboptimal algorithms that search for relatively high-quality subsets with reasonable computational effort.…”

EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection

“…BS methods can basically be summarized as supervised [6] and unsupervised [7] methods according to whether prior knowledge is required. Since prior knowledge is often difficult to obtain in practice, unsupervised BS methods have attracted extensive attention in recent decades.…”

A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

“…One of the key pretreatment task is to detect information redundancy hidden in hyperspectral, which seriously affects the extraction of real information of spectrum [13]. A lot of voluntary works have focused on this subject [13][14][15][16]. Of which, Liu et al [13] investigated the redundance hidden in the fluorescence spectrum through principal component regression analysis and moving windows selection in chemometrics; Liu et al [14] proposed a weighted maximum relevance minimum redundancy waveband selection algorithm to calculate the mutual information between wavebands and target classes and wavebands, which was used for classification of soybean hyerspectral imaging datasets.…”