The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM

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A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011, background): “This is particularly critical in very high spatial and spectral resolution image classification, which deal with large and/or complex features spaces using limited training information only [14].”
Fusion of Support Vector Machines for Classification of Multisensor Data (2007, methodology): “, artificial neural networks (ANN) [5], [7], self-learning decision trees (DT) [8], [9], and support vector machines (SVM) [10], [11].”
Support vector machines in remote sensing: A review (2011, unknown): “The authors recommended use of RVMs in other applications that involve estimation of bio-physical parameters using remotely sensed data because of its robustness to even small amount of training data and low sensitivity to free parameter setting.”
Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels (2014, methodology): “Several kernels are used in the literature (Foody and Mathur, 2006).”