Classification with spatio-temporal interpixel class dependency contexts
IEEE Transactions on Geoscience and Remote SensingPublished 1 July 1992
Byeungwoo Jeon, D. A. Landgrebe
Citations99
SJR quartileQ1
SJR score2.40
SNIP2.37
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TL;DR
A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated and should find use in many applications of remote sensing, especially when the classification accuracy is important.
Abstract
A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.>
Keywords
Computer ScienceEngineeringEnvironmental Science
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