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Local or global? How to choose the training set for principal component compression of hyperspectral satellite measurements. A hybrid approach

机译：本地还是全球？如何为高光谱卫星测量的主分量压缩选择训练集。混合方法

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Principal Component (PC) compression is the method of choice to achieve band-width reduction for dissemination of hyper spectral (HS) satellite measurements and will become increasingly important with the advent of future HS missions (such as IASI-NG and MTG-IRS) with ever higher data-rates. It is a linear transformation defined by a truncated set of the leading eigenvectors of the covariance of the measurements as well as the mean of the measurements. We discuss the strategy for generation of the eigenvectors, based on the operational experience made with IASI. To compute the covariance and mean, a so-called training set of measurements is needed, which ideally should include all relevant spectral features. For the dissemination of IASI PC scores a global static training set consisting of a large sample of measured spectra covering all seasons and all regions is used. This training set was updated once after the start of the dissemination of IASI PC scores in April 2010 by adding spectra from the 2010 Russian wildfires, in which spectral features not captured by the previous training set were identified. An alternative approach, which has sometimes been proposed, is to compute the eigenvectors on the fly from a local training set, for example consisting of all measurements in the current processing granule. It might naively be thought that this local approach would improve the compression rate by reducing the number of PC scores needed to represent the measurements within each granule. This false belief is apparently confirmed, if the reconstruction scores (root mean square of the reconstruction residuals) is used as the sole criteria for choosing the number of PC scores to retain, which would overlook the fact that the decrease in reconstruction score (for the same number of PCs) is achieved only by the retention of an increased amount of random noise. We demonstrate that the local eigenvectors retain a higher amount of noise and a lower amount of atmospheric signal than global eigenvectors. Local eigenvectors do not increase the compression rate, but increase the amount of atmospheric loss and should be avoided. Only extremely rare situations, resulting in spectra with features which have not been observed previously, can lead to problems for the global approach. To cope with such situations we investigate a hybrid approach, which first apply the global eigenvectors and then apply local compression to the residuals in order to identify and disseminate in addition any directions in the local signal, which are orthogonal to the subspace spanned by the global eigenvectors.
机译：主分量（PC）压缩是实现带宽减小以传播高光谱（HS）卫星测量值的首选方法，并且随着未来HS任务（如IASI-NG和MTG-IRS）的到来将变得越来越重要具有更高的数据速率。它是由测量的协方差以及测量平均值的前导特征向量的截短集合定义的线性变换。我们将基于IASI的运营经验，讨论特征向量的生成策略。为了计算协方差和均值，需要一个所谓的训练测量集，理想情况下，它应该包括所有相关的光谱特征。为了传播IASI PC分数，使用了一个全球静态培训集，该培训集由覆盖所有季节和所有地区的大量测量光谱样本组成。 IASI PC分数于2010年4月开始传播后，通过添加2010年俄罗斯野火的光谱对本训练集进行了一次更新，其中确定了先前训练集未捕获的光谱特征。有时已经提出的另一种方法是从本地训练集中即时计算特征向量，例如由当前处理颗粒中的所有测量值组成。可能天真地认为这种局部方法将通过减少表示每个颗粒内测量值所需的PC分数的数量来提高压缩率。如果将重建分数（重建残差的均方根）用作选择要保留的PC分数数量的唯一标准，则显然会证实这种错误信念，而这会忽略重建分数降低的事实（对于相同数量的PC）只能通过保留更多数量的随机噪声来实现。我们证明，与全局特征向量相比，局部特征向量保留更高的噪声量和更低的大气信号量。局部特征向量不会增加压缩率，但会增加大气损失量，应避免使用。只有极少数情况会导致光谱具有以前未曾观察到的特征，才可能导致整体方法出现问题。为了应对这种情况，我们研究了一种混合方法，该方法首先应用全局特征向量，然后对残差应用局部压缩，以识别和传播与局部区域正交的子空间正交的局部信号。特征向量。

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