基于改进型无监督蜉蝣优化算法的高光谱图像分类自适应特征选择

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mohammed Abdulmajeed Moharram, Divya Meena Sundaram
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引用次数: 0

摘要

高光谱成像是遥感科学的重要工具,能有效地划分感兴趣的区域。然而,高光谱图像(HSI)的分类遇到了显著的挑战,包括高度相关波段的高维度和训练样本的稀缺。通过确定最相关的波段以及利用未标记的训练样本来应对这些挑战是非常必要的。针对这些问题,本研究提出了一种基于增强型蜉蝣优化算法(MOA)的无监督框架,以选择信息量最大的频谱带。增强型 MOA 通过利用随机解决方案来探索全局搜索空间,并增强解决方案的多样性,从而有效地识别出信息量最大的频段。另一方面,利用最佳经验促进局部搜索,从而有效地获得最优解。这种平衡的探索-利用策略确保了算法在解决优化问题时的稳健性和有效性。最后,利用两种机器学习分类器在像素级高光谱图像分类中演示了所提出的方法:随机森林和支持向量机。在三个基准高光谱数据集上进行的深入实验一致证实了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Adaptive feature selection for hyperspectral image classification based on Improved Unsupervised Mayfly optimization Algorithm

Adaptive feature selection for hyperspectral image classification based on Improved Unsupervised Mayfly optimization Algorithm

Hyperspectral imaging has appeared as a vital tool in remote sensing science for its efficacy in effectively delineating regions of interest. However, the classification of hyperspectral images (HSI) encounters notable challenges, including the high dimensionality of highly correlated bands and the scarcity of training samples. Addressing these challenges is very essential by determining the most relevant bands, as well as the utilization of unlabelled training samples. In response to these issues, this study presents an unsupervised framework based on an enhanced Mayfly Optimization Algorithm (MOA) in order to select the most informative spectral bands. The enhanced MOA effectively identifies informative bands by leveraging the random solutions to explore the global search space, and enhance the solution diversity. On the other hand, leveraging the best experiences to boost the local search, efficiently attaining optimal solutions. This balanced exploration-exploitation strategy ensures the algorithm’s robustness and effectiveness in addressing the optimization problem. Ultimately, the proposed approach is demonstrated at the pixel-level hyperspectral image classification using two machine learning classifiers: Random Forest and Support Vector Machine. Thorough experimentation carried out on three benchmark hyperspectral datasets consistently confirms the effectiveness of the proposed approach.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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