Particle Swarms for Feature Extraction of Hyperspectral Data
Particle Swarms for Feature Extraction of Hyperspectral Data
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DOI：
10.1093/ietisy/e90-d.7.1038
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发表时间：
2007-07
期刊：
影响因子：
--
通讯作者：
S. Monteiro;Y. Kosugi
中科院分区：
文献类型：
--
作者：
S. Monteiro;Y. Kosugi
提出了一种新的基于粒子群的高光谱图像特征提取算法。粒子群优化算法最初是为解决连续空间上的全局优化问题而发展起来的，本文将其扩展到特征提取问题。开发了一种利用两群粒子的公式，以同时优化所需的性能标准和所选特征的数量。在回归问题上评估候选特征集。利用人工神经网络建立大豆作物中葡萄糖化学浓度的线性和非线性模型。利用真实世界的高光谱数据集的实验结果证明了该方法的可行性。与传统的特征提取方法相比，基于粒子群的方法在线性和非线性模型上表现出上级性能。
This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.