Skauli Torbjørn
Norwegian defence research establishment, Kjeller, Norway.
Opt Express. 2012 Jan 16;20(2):918-33. doi: 10.1364/OE.20.000918.
Coregistration errors in multi- and hyperspectral imaging sensors arise when the spatial sensitivity pattern differs between bands or when the spectral response varies across the field of view, potentially leading to large errors in the recorded image data. In imaging spectrometers, spectral and spatial offset errors are customarily specified as "smile" and "keystone" distortions. However these characteristics do not account for errors resulting from variations in point spread function shape or spectral bandwidth. This paper proposes improved metrics for coregistration error both in the spatial and spectral dimensions. The metrics are essentially the integrated difference between point spread functions. It is shown that these metrics correspond to an upper bound on the error in image data. The metrics enable estimation of actual data errors for a given image, and can be used as part of the merit function in optical design optimization, as well as for benchmarking of spectral image sensors.
当多光谱和高光谱成像传感器中各波段间的空间灵敏度模式不同，或者光谱响应在整个视场范围内变化时，就会出现配准误差，这可能会导致记录的图像数据出现较大误差。在成像光谱仪中，光谱和空间偏移误差通常被指定为“微笑”和“梯形失真”。然而，这些特性并未考虑到点扩散函数形状变化或光谱带宽变化所导致的误差。本文提出了在空间和光谱维度上改进的配准误差度量。这些度量本质上是点扩散函数之间的积分差异。结果表明，这些度量对应于图像数据误差的上限。这些度量能够估计给定图像的实际数据误差，并且可以用作光学设计优化中品质因数的一部分，以及光谱图像传感器的基准测试。
