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Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers

机译:空气传播光谱仪高光谱数据噪声建模与估计

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摘要

The definition of noise models suitable for hyperspectral data is slightly different depending on whether whiskbroom or push-broom are dealt with. Focussing on the latter type (e.g., VIRS-200) the noise is intrinsically non-stationary in the raw digital counts. After calibration, i.e. removing the variability effects due to different gains and offsets of detectors, the noise will exhibit stationary statistics, at least spatially. Hence, separable 3D processes correlated across track (x), along track (y) and in the wavelength (?), modelled as auto-regressive with GG statistics have been found to be adequate. Estimation of model parameters from the true data is accomplished through robust techniques relying on linear regressions calculated on scatter-plots of local statistics. An original procedure was devised to detect areas within the scatter-plot corresponding to statistically homogeneous pixels. Results on VIRS-200 data show that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and somewhat correlated along and across track by slightly different extents. Spectral correlation has been investigated as well and found to depend both on the sparseness (spectral sampling) and on the wavelength values of the bands that have been selected.
机译:适用于高光谱数据的噪声模型的定义取决于否须经蜡组织或推扫帚是否处理。在后一型(例如,VIR-200)上侧重于原始数字计数的噪声是本质上非静止的。校准后,即除了探测器的不同增益和偏移引起的可变性效应,噪声将在空间上表现出静止统计数据。因此,已经发现,已经发现以轨道(x)沿轨道(x)和波长(Δ)相关的可分离的3D处理,以与GG统计数据建模为自动回归。通过依赖于在局部统计的散射图上计算的线性回归的鲁棒技术来实现来自真实数据的模型参数。设计了原始程序,以检测与统计上同质像素对应的散射图内的区域。 VIR-200数据的结果表明,噪声是重尾(尾部比高斯PDF的尾部),并且通过略微不同的范围横跨轨道相关。还研究了光谱相关性,发现依赖于稀疏度(光谱采样)和所选择的带的波长值。

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