Abstract Hyperspectral images generally contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar land cover classes. However, such high dimensional data also contains highly correlated but irrelevant band information. Selection of useful bands is an effective approach to alleviate the curse of dimensionality of hyperspectral image classification. In this manuscript, band selection problem is formulated as a combinatorial optimization problem. Cuckoo search (CS) is one of the widely used, effective nature inspired algorithm in global optimization approaches. In spite of its efficiency, the homogeneous search behaviour of the standard CS leads to slower convergence and gets easily trapped into a local optimal solution. A modified CS (MCS) algorithm is proposed for dealing with these drawbacks and solving the problem of hyperspectral band selection. The proposed algorithm uses the Chebyshev chaotic map to initialize the nest locations at initial step. Meanwhile, the step size is adaptively adjusted based on fitness value and current iteration number. The experimental results on two standard benchmark datasets namely, Pavia University and Indian Pines, prove the superiority of the proposed method over standard CS approach as well as the other traditional approaches in terms of average accuracy, overall accuracy, Cohen’s kappa coefficient (κ), statistical significance assessment using McNemar’s test, and fitness curve analysis. The proposed technique has achieved the maximum overall accuracy of 95.10% for Pavia University dataset, and 86.92% for Indian Pines dataset.

中文翻译：

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基于元启发式优化方法的高光谱波段选择

摘要 高光谱图像通常包含数百个连续的光谱带，可以精确区分各种光谱相似的土地覆盖类别。然而，这样的高维数据也包含高度相关但不相关的波段信息。选择有用波段是缓解高光谱图像分类维度灾难的有效方法。在这篇手稿中，波段选择问题被表述为一个组合优化问题。布谷鸟搜索 (CS) 是全局优化方法中广泛使用的有效自然启发算法之一。尽管效率很高，但标准 CS 的同构搜索行为导致收敛速度较慢，并且很容易陷入局部最优解。提出了一种改进的 CS (MCS) 算法来处理这些缺点并解决高光谱波段选择问题。所提出的算法在初始步骤使用切比雪夫混沌图来初始化巢穴位置。同时，根据适应度值和当前迭代次数自适应调整步长。在帕维亚大学和印度松树这两个标准基准数据集上的实验结果证明了所提出的方法在平均准确度、整体准确度、科恩卡帕系数 (κ) 方面优于标准 CS 方法以及其他传统方法，使用 McNemar 检验和适应度曲线分析进行统计显着性评估。所提出的技术在帕维亚大学数据集上达到了 95.10% 的最大总体准确率，达到了 86%。