Implementation of temporal relationships in knowledge based classification of satellite images.
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TL;DR
A method of knowledge-based classification based on temporal relationships between classes is introduced and knowledge about crop rotations is represented by means of state transition matrices in a Baysian maximum-likelihood classification of digital satellite images.
Abstract
Classification of digital satellite images involves a process similar to classification problems in artificial intelligence. In a knowledge-based classification, ancillary data and knowledge are combined with spectral information. A method of knowledge-based classification based on temporal relationships between classes is introduced. Knowledge about crop rotations is represented by means of state transition matrices. Spectral image information, information stored in a GIS, and knowledge as represented in a matrix are combined in a Baysian maximum-likelihood classification.