Parthenium hysterophorus (an alien weed) is posing serious threat to crop yields, livestock's and human health and is considered the seventh most devastating weed across the globe. Early identification and mapping of Parthenium hysterophorus is essential for the timely eradication and control of this alien invasive species. Hyperspectral remote sensing (due to its high spectral details) is highly valuable in discriminating vegetation species and mapping its distribution. However, the use of high-dimensional hyperspectral data possesses the threat of multi-collinearity (i.e., contiguous wavelength-bands exhibits strong spectral correlation) which in-turn yields unstable parameter estimation. This study aims to explore the potential of hyperspectral (spectroscopic) data for discerning Parthenium hysterophorus and to identify optimal wavebands that are sensitive for species discrimination. In this study, the spectral signatures of Parthenium hysterophorus and four co-occurring plant species were acquired using portable hand held spectrometer. Spectral Angle Mapper (SAM) in conjunction with Genetic Algorithms (GA) were used discern the measured species based on their spectral profiles. The analysis yielded high classification accuracies for both the training (overall accuracy = 99%) and testing (overall accuracy = 97%) datasets. The SAM-GA picked a meaningful subset of spectral bands from different parts of electromagnetic spectrum (i.e., centering at 0.47 μm, 0.715 μm, 1.12–1.25 μm and 1.8–1.9 μm) which carries highest information for the spectral discrimination of Parthenium hysterophorus. In conclusion, this study confirms the capability of hyperspectral data in discerning Parthenium hysterophorus from other crops/plant species and also highlights the importance of few wavelength positions for the spectral discrimination of Parthenium hysterophorus weed.

Parthenium hysterophorus（一种外来杂草）对作物产量、牲畜和人类健康构成严重威胁，被认为是全球第七大最具破坏性的杂草。Parthenium hysterophorus 的早期识别和测绘对于及时根除和控制这种外来入侵物种至关重要。高光谱遥感（由于其高光谱细节）在区分植被物种和绘制其分布图方面非常有价值。然而，高维高光谱数据的使用具有多重共线性的威胁（即，连续的波段表现出强烈的光谱相关性），这反过来会产生不稳定的参数估计。本研究旨在探索高光谱（光谱）数据在辨别Parthenium hysterophorus并确定对物种区分敏感的最佳波段。在这项研究中，使用便携式手持光谱仪获得了Parthenium hysterophorus和四种共生植物的光谱特征。光谱角度映射器 (SAM) 与遗传算法 (GA) 结合使用，可根据光谱分布来辨别测量的物种。该分析为训练（总准确率 = 99%）和测试（总准确率 = 97%）数据集产生了高分类准确率。SAM-GA 从电磁波谱的不同部分（即以 0.47 μm、0.715 μm、1.12-1.25 μm 和 1.8-1.9 μm 为中心）挑选了一个有意义的谱带子集，这些谱带携带了用于光谱鉴别的最高信息Parthenium hysterophorus。总之，这项研究确认高光谱数据的辨别能力银胶菊从其他作物/植物品种，也凸显一些波长为位置的光谱歧视的重要性银胶菊杂草。