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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Coupled Subspace-Based Atmospheric Compensation of LWIR Hyperspectral Data
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Coupled Subspace-Based Atmospheric Compensation of LWIR Hyperspectral Data

机译：LWIR高光谱数据的基于子空间的大气补偿

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This paper deals with atmospheric correction in hyperspectral data acquired in the long-wave infrared (LWIR) spectral range. Atmospheric compensation (AC) is approached from a new perspective in that it is reformulated as an estimation problem on a low-rank subspace. Specifically, a subspace-based model is exploited to represent both the atmospheric transmittance and the upwelling radiance. Taking into account the inherent correlation between these two quantities, we adopt a subspace model that constrains them to vary in a physically consistent way. Exploiting such a model, AC is formulated as a nonlinear least-squares estimation problem. An automatic procedure is proposed to estimate the unknown parameters, which is based only on the image analysis and does not need any atmospheric measurements. Results of an extensive experimental analysis carried out on simulated data are used to discuss the performance of the algorithm with respect to different atmospheric conditions. Finally, experimental evidence over a real LWIR hyperspectral image is provided.
机译：本文涉及在长波红外（LWIR）光谱范围内获取的高光谱数据中的大气校正。大气补偿（AC）是从一个新的角度出发的，因为它被重新表述为低秩子空间上的一个估计问题。具体来说，利用基于子空间的模型来表示大气透射率和上升流辐射率。考虑到这两个量之间的固有相关性，我们采用子空间模型来约束它们以物理上一致的方式变化。利用这种模型，将AC公式化为非线性最小二乘估计问题。提出了一种自动程序来估计未知参数，该程序仅基于图像分析并且不需要任何大气测量。在模拟数据上进行的广泛实验分析的结果用于讨论算法在不同大气条件下的性能。最后，提供了有关实际LWIR高光谱图像的实验证据。

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