Passive hyperspectral long-wave infrared measurements are remarkably
informative about the surroundings, such as remote object material composition,
temperature, and range; and air temperature and gas concentrations. Remote
object material and temperature determine the spectrum of thermal radiance, and
range, air temperature, and gas concentrations determine how this spectrum is
modified by propagation to the sensor. We computationally separate these
phenomena, introducing a novel passive range imaging method based on
atmospheric absorption of ambient thermal radiance. Previously demonstrated
passive absorption-based ranging methods assume hot and highly emitting
objects. However, the temperature variation in natural scenes is usually low,
making range imaging challenging. Our method benefits from explicit
consideration of air emission and parametric modeling of atmospheric
absorption. To mitigate noise in low-contrast scenarios, we jointly estimate
range and intrinsic object properties by exploiting a variety of absorption
lines spread over the infrared spectrum. Along with Monte Carlo simulations
that demonstrate the importance of regularization, temperature differentials,
and availability of many spectral bands, we apply this method to long-wave
infrared (8--13 $\mu$m) hyperspectral image data acquired from natural scenes
with no active illumination. Range features from 15m to 150m are recovered,
with good qualitative match to unaligned lidar data.

通过对范围成像方法进行分析和模型建立，我们提出了一种新颖的被动范围成像方法，它利用大气吸收环境热辐射，在低对比度场景下联合估计范围和固有目标属性，并成功地将此方法应用于无主动照明的自然场景中的长波红外高光谱图像数据。