A Band Selection Method for Hyperspectral Image Based on Binary Coded Hybrid Rice Optimization Algorithm

Ye, Zhiwei; Liu, Shiqin; Zong, Xinlu; Shu, Zhe; Xia, Xiaoyu

doi:10.1109/idaacs53288.2021.9661017

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…It can be seen from Figure 7 that the fitness function values of GA, CS and FA are only slight fluctuations throughout the iteration, indicating that they easily fall into local optimum at the early stage of iteration. In contrast, the fitness value of MHRO is always the lowest and keeps a decreasing trend, which implies that MHRO has strong optimization ability and is superior to algorithms GA [28], PSO [32], CS [33], FA [34] and HRO [38].…”

Section: Discussionmentioning

confidence: 99%

“…Appropriate parameter settings can improve the optimization ability of the algorithm. In the following experiments, the proposed band selection technique is compared with GA [28], PSO [32], CS [33], FA [34] and HRO [38]. The corresponding parameter settings of each algorithm are listed in Table 6.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…Su et al [37] designed two different hybrid models for the complex large-scale 0-1 knapsack problem by using novel combinations of improved HRO and binary ant colony algorithm, which achieved better performance on different size datasets. In addition, Ye et al [38] regarded the band selection problem as a combinatorial optimization problem and employed binary HRO to select the optimal band set for HSI, which obtained good results in classification precision and execution efficiency. Although HRO algorithm has contributed to acquiring satisfactory results, primary HRO performs the exploitation of the current best solution during each search process inadequately.…”

Section: Introductionmentioning

confidence: 99%

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A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

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Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

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