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Target Spectrum based Feature Selection (TSFS): a new method based on chain coding for target detection problems
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.infrared.2020.103429
A.H. Houshyaripour , M. Momeni

Abstract One of the controversial issues in hyperspectral remote sensing methods for target detection is whether the feature selection will be useful. Generally, feature selection methods are divided into variance based and wavelength based methods. Variance based feature selection methods like information-theory-based methods may eliminate the distinctive features because the distinctive features probably are not statistical principal components. Distinctive features are crucial to distinguish target from background and are maintained in wavelength based methods which concentrate on wavelength information. However, beside to wavelength-based information, target spectrum fluctuations are also critical for target detection. In addition, the wavelength based methods are often time consuming iterative methods with high computational cost. This paper introduces a new feature selection method considering hyperspectral target spectrum. The proposed algorithm has been developed based on Chain coding idea. We proposed Chain Filtering, Chain Encoding, and Chain Statistics as filter, embedded, and wrapper feature selection methods. In this paper, Chain filtering, Chain statistics, and Chain encoding approaches have been compared with different types of feature selection methods such as Principle Component Analysis (PCA) and Minimum Noise Fraction (MNF). Numerical tests have been executed using 4 datasets including Cuprite Nevada and Jasper Ridge datasets from AVIRIS, Botswana, and local datasets of Isfahan province from Hyperion sensor and using Constrained Energy Minimization (CEM) target detection. The results show the accuracy of target detection applying the proposed feature selection method increases from 85% to about 92% for Kaolinite and from 77% to 96% for Buddingtonite in comparison with PCA for Cuprite dataset. Furthermore, the increments more than 5% and 17.5% will be achieved in comparison with MNF, respectively. The results have shown that not only the proposed method overcomes the accuracy decrement issue of feature selection in target detection, but also it improves target detection accuracy by eliminating non-informative features for target detection applications. So feature selection will be an efficient tool for target detection if the applied feature selection method picks out the distinctive features well.

中文翻译:

Target Spectrum based Feature Selection (TSFS):一种基于链式编码的目标检测问题新方法

摘要 用于目标检测的高光谱遥感方法中存在争议的问题之一是特征选择是否有用。通常,特征选择方法分为基于方差和基于波长的方法。基于方差的特征选择方法(如基于信息理论的方法)可能会消除显着特征,因为显着特征可能不是统计主成分。独特的特征对于区分目标和背景至关重要,并且在专注于波长信息的基于波长的方法中得到维护。然而,除了基于波长的信息外,目标光谱波动对于目标检测也很关键。此外,基于波长的方法通常是耗时且计算成本高的迭代方法。本文介绍了一种新的考虑高光谱目标光谱的特征选择方法。所提出的算法是基于链编码思想开发的。我们提出了链过滤、链编码和链统计作为过滤、嵌入和包装特征选择方法。在本文中,链过滤、链统计和链编码方法与不同类型的特征选择方法,如主成分分析 (PCA) 和最小噪声分数 (MNF) 进行了比较。使用 4 个数据集进行了数值测试,包括来自博茨瓦纳 AVIRIS 的 Cuprite Nevada 和 Jasper Ridge 数据集,以及来自 Hyperion 传感器的伊斯法罕省本地数据集,并使用了约束能量最小化 (CEM) 目标检测。结果表明,与铜矿数据集的 PCA 相比,应用所提出的特征选择方法的目标检测精度对于高岭石从 85% 提高到约 92%,对于 Buddingtonite 从 77% 提高到 96%。此外,与MNF相比,将分别实现超过5%和17.5%的增量。结果表明,所提出的方法不仅克服了目标检测中特征选择的精度下降问题,而且通过消除目标检测应用中的非信息特征来提高目标检测精度。因此,如果应用的特征选择方法能够很好地挑选出显着特征,特征选择将成为目标检测的有效工具。
更新日期:2020-09-01
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