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High Resolution Hyperspectral Imaging with a High Throughput Virtual Slit

机译:具有高通量虚拟狭缝的高分辨率高光谱成像

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Hyperspectral imaging (HSI) device users often require both high spectral resolution, on the order of 1 nm, and high light-gathering power. A wide entrance slit assures reasonable etendue but degrades spectral resolution. Spectrometers built using High Throughput Virtual Slit™ (HTVS) technology optimize both parameters simultaneously. Two remote sensing use cases that require high spectral resolution are discussed. First, detection of atmospheric gases with intrinsically narrow absorption lines, such as hydrocarbon vapors or combustion exhaust gases such as NOx and CO_2. Detecting exhaust gas species with high precision has become increasingly important in the light of recent events in the automobile industry. Second, distinguishing reflected daylight from emission spectra in the visible and NIR (VNIR) regions is most easily accomplished using the Fraunhofer absorption lines in solar spectra. While ground reflectance spectral features in the VNIR are generally quite broad, the Fraunhofer lines are narrow and provide a signature of intrinsic vs. extrinsic illumination. The High Throughput Virtual Slit enables higher spectral resolution than is achievable with conventional spectrometers by manipulating the beam profile in pupil space. By reshaping the instrument pupil with reflective optics, HTVS-equipped instruments create a tall, narrow image profile at the exit focal plane, typically delivering 5X or better the spectral resolution achievable with a conventional design.
机译:高光谱成像(HSI)设备的用户通常既需要大约1 nm的高光谱分辨率,又需要很高的聚光能力。宽的入口狭缝可确保合理的集光率,但会降低光谱分辨率。使用High Throughput Virtual Slit™(HTVS)技术构建的光谱仪可同时优化两个参数。讨论了需要高光谱分辨率的两个遥感用例。首先,使用本质上较窄的吸收线(例如碳氢化合物蒸气)或燃烧废气(例如NOx和CO_2)检测大气气体。鉴于汽车工业中的最新事件,高精度地检测废气种类变得越来越重要。其次,使用太阳光谱中的Fraunhofer吸收线最容易实现将反射的日光与可见光和NIR(VNIR)区域中的发射光谱区分开。虽然VNIR中的地面反射光谱特征通常很宽,但Fraunhofer线很窄,并提供了本征与非本征照明的特征。通过处理光瞳空间中的光束轮廓,高通量虚拟狭缝可实现比传统光谱仪更高的光谱分辨率。通过使用反射光学器件对仪器的瞳孔进行整形,配备HTVS的仪器可在出射焦平面上产生高而窄的图像轮廓,通常可提供传统设计所能达到的5倍或更高的光谱分辨率。

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