Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Mapping a specific class with an ensemble of classifiers
International Journal of Remote Sensing
Expert system classification of urban land use/cover for Delhi, India
International Journal of Remote Sensing
Estimating per-pixel thematic uncertainty in remote sensing classifications
International Journal of Remote Sensing
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Remote sensing has considerable potential as a source of data for land cover mapping. This potential remains to be fully realised due, in part, to the methods used to extract land cover information from the remotely sensed data. Widely used statistical classifiers provide a poor representation of land cover, make untenable assumptions about the data and convey no information on the quality of individual class allocations. This paper shows that a softened classification, providing information on the strength of membership to all classes for each image pixel, may be derived from a neural network. This information may be used to indicate classification quality on a per-pixel basis. Moreover, a soft or fuzzy classification may be derived to more appropriately represent land cover than the conventional hard classification.