ABSTRACT

The successful implementation of Spectral Matching Measures (SMMs) often plays a crucial role in material discrimination and classification using hyperspectral dataset. The commonly exploited SMMs, such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and their hybrid, i.e., SIDSAM_tan, show limited discrimination ability while discriminating spectrally similar materials. This study presents three new effective SMMs named Dice Spectral Similarity Coefficient (DSSC), Kumar–Johnson Spectral Similarity Coefficient (KJSSC), and a hybrid of DSSC and KJSSC, i.e., KJDSSC_tan, for accurate discrimination of spectrally similar materials. A wide range of hyperspectral datasets of minerals and vegetation acquired under laboratory and real atmospheric conditions were used to compare and evaluate the performance of newly proposed and existing SMMs using Relative Spectral Discrimination Power (RSDPW) statistics. We also assessed the discrimination ability of the proposed and existing SMMs using spectra of selected minerals and vegetation species with an added component of random noise and linearly synthesized mixed spectra. An in-depth comparison and evaluation of different SMMs demonstrated that the discrimination power of the proposed SMMs is significantly higher than existing SMMs. The proposed SMMs also outperform existing SMMs when discriminating noisy and linearly synthesized mixed counterparts. The KJSSC and DSSC show similar efficacy in discriminating spectra of minerals and vegetation, whereas their hybrid measure, i.e., KJDSSC_tan shows significantly higher spectral discrimination ability. Therefore, the newly proposed hybrid measure, i.e., KJDSSC_tan is recommended over existing SMMs for successful material discrimination and classification using hyperspectral data.

摘要

光谱匹配措施（SMM）的成功实施通常在使用高光谱数据集进行材料区分和分类中起着至关重要的作用。常用的SMM（例如光谱角映射器（SAM），光谱信息散度（SID）及其混合模型，即SIDSAM _tan）在区分光谱相似材料时显示出有限的区分能力。这项研究提出了三个新的有效SMM，分别称为Dice光谱相似系数（DSSC），Kumar-Johnson光谱相似系数（KJSSC），以及DSSC和KJSSC的混合体，即KJDSSC _tan，用于准确区分光谱相似的材料。在实验室和实际大气条件下采集的各种矿物和植被的高光谱数据集，用于使用相对光谱鉴别力（RSDPW）统计数据来比较和评估新提议和现有的SMM的性能。我们还使用选定的矿物和植被物种的光谱，加上随机噪声和线性合成的混合光谱，评估了提议的和现有的SMM的分辨能力。对不同SMM的深入比较和评估表明，提出的SMM的辨别力明显高于现有SMM。当区分嘈杂的和线性合成的混合对应物时，建议的SMM也优于现有的SMM。_棕褐色显示出明显更高的光谱辨别能力。因此，对于现有的SMM，建议使用新提议的混合测量方法，即KJDSSC _tan，以成功地使用高光谱数据进行材料区分和分类。