Multiple classifiers applied to multisource remote sensing data

By Gunnar Jakob Briem, Jon Atli Benediktsson, Johannes R. Sveinsson. IEEE Transactions on Geoscience and Remote Sensing, 2002

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A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, background): “Areas of application have a wide-range remote sensing [18-21], medical image processing [22], target and object recogni-294 tion [23], face recognition [24-26], speech processing and video classification and retrieval [27], and gene detection in DNA sequences [28].”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, background): “MCSs were recently reviewed in the context of remote sensing [22] and yield excellent results when dealing with hyperspectral and multisource data sets [8], [9], [23]–[25].”
Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery (2008, background): “But Adaboost was also described as susceptible to noise and more costly in processing time (Briem et al., 2002).”
Fusion of Support Vector Machines for Classification of Multisensor Data (2007, result): “compared to accuracies for single classifiers [9], [40], [41].”