Hyperspectral Data Processing: Algorithm Design and Analysis

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Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, methodology): “In particular, from the input dataset point of view, several algorithms for dataset processing, such as normalization or dimensionality reduction, have been developed, and are applied to improve the classiﬁcation performance [162–164] .”
An Effective Evaluation Tool for Hyperspectral Target Detection: 3D Receiver Operating Characteristic Curve Analysis (2021, background): “6 was particularly selected in favor of other data sets in the sense that it is well-studied along with precise knowledge of spatial locations of all 19 R target panel pixels provided by ground truth [19], [23], [29], [32].”
Deep supervised learning for hyperspectral data classification through convolutional neural networks (2015, background): “eu imaging [1], it has its own unique challenges including; i) high dimensional data, ii) limited number of labeled samples and iii) large spatial variability of spectral signatures [2].”