Title :
A Markov random field-based approach to decision-level fusion for remote sensing image classification
Author_Institution :
Fac. of Integrated Arts & Sci., Hiroshima Univ., Japan
Abstract :
A method is proposed for the enhancement of the quality of a classification result by fusing this result with remote sensing images, based on a Markov random field approach. The classification accuracy is estimated by a modified posterior probability, which is used for choosing the optimal classification result. The procedure is applied to a benchmark dataset for discrimination provided by the IEEE Geoscience and Remote Sensing Society Data Fusion Committee, and it shows an excellent performance. The classified result won the competition of the data fusion contest 2001 held by the same committee.
Keywords :
Markov processes; geophysical signal processing; image classification; image enhancement; random processes; remote sensing; sensor fusion; Markov random field-based approach; decision-level fusion; enhancement; image segmentation; modified posterior probability; optimal classification; quality; remote sensing image classification; Gaussian distribution; Geoscience and Remote Sensing Society; Image classification; Image segmentation; Markov processes; Markov random fields; Probability distribution; Remote sensing; Training data; Voting;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.816648
