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This book reviews the state of the art in cutting-edge algorithmic approaches addressing the challenges to robust hyperspectral image analytics, with a focus on new trends in machine learning and image processing/understanding. It presents research from leading international experts who have made foundational contributions to areas such as machine learning, super-resolution, subspace learning, spectral unmixing, manifold learning, and target recognition as applied to hyperspectral image analysis.
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