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Spatio-spectral deconvolution for high resolution spectral imaging with an application to the estimation of sun-induced fluorescence
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-10-06 , DOI: 10.1016/j.rse.2021.112718
Hanno Scharr 1, 2 , Patrick Rademske 1 , Luis Alonso 3 , Sergio Cogliati 4 , Uwe Rascher 1
Affiliation  

We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are measured at different pixel positions of the imaging sensor, i.e. at different spatio-spectral locations, and averaged in order to get an as accurate PSF as possible. We investigate error sources in this calibration process by simulating the procedure in silico. Averaging as well as the spectral and spatial width of the point source introduce some smoothness in the measured PSF. We propose corrective measures, i.e. deconvolution of the PSF itself and median instead of mean averaging, leading to a set of sharper PSFs. We test the performance of these PSFs in deconvolving simulated as well as real hyperspectral images. For deconvolution we test a set of well-known, off the shelf deconvolution algorithms. Quantitatively in terms of PSNR (Peak Signal to Noise Ratio) a combination of Wiener filtering and sharpened PSFs yields strongest improvements, while using Wiener filtering with non-sharpened PSFs even deteriorates the signal. Comparing deconvolution results of the simulated data with results of real data reveals, that visually very similar effects can be observed. This well supports the assumption, that our findings are also valid for real spatio-spectral data. Surprisingly, the choice of PSF, sharpened or not, is of little effect for SIF estimation with the iFLD algorithm in the O2A band. Quantitatively we find that deconvolution reduces the overall error of SIF by a factor of 3.8, when using Wiener filtering instead of the currently used 1 iteration of vanCittert's method. For SIF estimation in the O2B band we observe a totally different behavior, where all deconvolution methods yield unreliable results with mostly well above 200% relative error and high standard deviations. In the discussion we can only speculate on possible reasons for this unreliability. As conclusion we therefore propose to use the O2A band for SIF estimation together with classic Wiener filtering for deconvolution of spatio-spectral data.



中文翻译:

高分辨率光谱成像的空间光谱解卷积,用于估计太阳诱导的荧光

我们提出了一种用于成像光谱仪数据的信号解卷积程序,其中测量的点扩散函数 (PSF) 在用于信号解卷积之前先进行解卷积。我们评估了我们改进空间光谱信号的程序的有效性,以及我们的目标应用,即太阳诱导荧光 (SIF) 的估计。成像光谱仪是用于遥感的成熟仪器。当用于科学目的时,这些仪器通常会定期校准。在我们的例子中,光学元件的点扩散函数是在一个复杂的程序中用可调单色器点光源测量的。PSF 在成像传感器的不同像素位置(即在不同的空间光谱位置)进行测量,并进行平均以获得尽可能准确的 PSF。我们通过在 silico 中模拟该过程来调查此校准过程中的误差源。平均以及点源的光谱和空间宽度会在测量的 PSF 中引入一些平滑度。我们提出了纠正措施,即 PSF 本身和中值的去卷积而不是平均,从而导致一组更清晰的 PSF。我们测试了这些 PSF 在去卷积模拟和真实高光谱图像中的性能。对于反卷积,我们测试了一组众所周知的现成反卷积算法。从数量上看 PSNR(峰值信噪比),维纳滤波和锐化 PSF 的组合产生了最强的改进,而使用维纳滤波和非锐化 PSF 甚至会使信号恶化。将模拟数据的解卷积结果与真实数据的结果进行比较显示,可以观察到视觉上非常相似的效果。这很好地支持了这样一个假设,即我们的发现也适用于真实的空间光谱数据。令人惊讶的是,PSF 的选择,无论是否锐化,对 O 中 iFLD 算法的 SIF 估计影响很小2乐队。从数量上我们发现,当使用维纳滤波而不是当前使用的 vanCittert 方法的 1 次迭代时,反卷积将 SIF 的整体误差降低了 3.8 倍。对于 O 2 B 波段中的 SIF 估计,我们观察到完全不同的行为,其中所有解卷积方法产生不可靠的结果,相对误差大多远高于 200% 和高标准偏差。在讨论中,我们只能推测这种不可靠性的可能原因。因此,作为结论,我们建议使用 O 2 A 频带进行 SIF 估计,并结合经典的 Wiener 滤波进行空间光谱数据的去卷积。

更新日期:2021-10-06
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