# Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods Source: [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:07:28.348Z Authors: Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, J\\'on Atli Benediktsson Published: 2013-10-18T17:49:45.000Z ## Abstract Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()