We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long‐wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.

中文翻译：

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用于固体检测的高光谱数据的稀疏高斯S形基函数逼近

我们定义了用于高光谱数据成分开发的发射率和反射率曲线的新特征。我们的方法使用惩罚回归将每个频谱分解为一组稀疏的高斯S形分量。检测基于目标材料特有的高斯乙状结肠成分的组合。关注光谱上坡和下坡的存在，而不是光谱相关性，使检测对目标变化和收集过程中遇到的大气和背景的光谱变化的鲁棒性更高。我们目前的模拟研究表明，在不损害灵敏度的情况下降低假阳性率的潜力。对长波红外（LWIR）实验数据的表征验证了我们使用不同粒径，测量角度，