Titlebook: Hyperspectral Image Analysis; Advances in Machine Saurabh Prasad,Jocelyn Chanussot Book 2020 Springer Nature Switzerland AG 2020 Hyperspec

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Machine Learning Methods for Spatial and Temporal Parameter Estimation, monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decis

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Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms, networks, along with their variants, is well documented for color image analysis. However, remote sensing and biomedical imaging often rely on hyperspectral images containing more than three channels for pixel-level characterization. Deep learning can facilitate image analysis in multi-channel imag

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,Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practiction—these are primarily applied to color imagery and video. In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. These multi-channel images come with their own unique set of challe

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Sparsity-Based Methods for Classification,er introduces the sparse representation methodology and its related techniques for hyperspectral image classification. To start with, we provide a brief review on the mechanism, models, and algorithms of sparse representation classification (SRC). We then introduce several advanced SRC methods that

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