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We present a new binary (two-class) supervised non-parametric classification approach that is based on iterative partitioning of multidimensional feature space into variably-sized and nested hyper-cubes (partitions). The proposed method contains elements of active learning and includes classifier to analyst queries. The spectral transition zone between two thematic classes (i.e., where training labels of different classes overlap in feature space) is targeted through iterative training derivation. Three partition categories are defined: pure, indivisible and unlabeled. Pure partitions contain training labels from only one class, indivisible partitions contain training data from different classes, and unlabeled partitions do not contain training data. A minimum spectral tolerance threshold defines the smallest partition volume to avoid over-fitting. In this way the transition zones between class distributions are minimized, thereby maximizing both the spectral volume of pure partitions in the feature space and the number of pure pixels in the classified image. The classification results are displayed to show each classified pixel's partition category (pure, unlabeled and indivisible). Mapping pixels belonging to unlabeled partitions serves as a query from the classifier to the analyst, targeting spectral regions absent of training data. The classification process is repeated until significant improvement of the classification is no longer realized or when no classification errors and unlabeled pixels are left. Variably-sized partitions lead to intensive training data derivation in the spectral transition zones between the target classes. The methodology is demonstrated for surface water and permanent snow and ice classifications using 30 m conterminous United States Landsat 7 Enhanced Thematic Mapper Plus (ETM +) data time series from 2006 to 2010. The surface water result was compared with Shuttle Radar Topography Mission (SRTM) water body and National Land Cover Database (NLCD) open water classes with an overall agreement greater than 99% and Kappa coefficient greater than 0.9 in both of cases. In addition, the surface water result was compared with a classification generated using the same input data and a standard bagged Classification and Regression Tree (CART) classifier. The nested segmentation and CART-generated products had an overall agreement of 99.9 and Kappa coefficient of 0.99.

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Remote Sensing of Environment



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10. 1016/j.rse.2015.04.022






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This article was published in Remote Sensing of Environment. (2015), 165.