Contextual approach for oil spill detection in SAR images using image fusion and markov random fields
Por:
López L., Moctezuma M., Parmiggiani F.
Publicada:
1 ene 2006
Resumen:
This paper presents a study for oil spill detection. The scheme incorporates contextual information using multiconexity analysis. The image is modeled as a discrete Markov Random Field (MRF). Each pixel can be classified in two classes: {oil, not-oil}. To determine the class we optimized the a posteriori energy function by means of simulated annealing. The segmentation result contains different levels of information. In order to improve the detection, we propose a data fusion stage. To realize the data fusion we use a contextual algorithm. The result obtained is binary and shows in detail the oil spill in the analysis zone. © 2006 IEEE.
Filiaciones:
López L.:
National University of Mexico, DIE-UNAM, Ciudad Universitaria, Apdo. Postal 70-256, Coyoacan, C.P. 04510, Mexico D.F
Moctezuma M.:
National University of Mexico, DIE-UNAM, Ciudad Universitaria, Apdo. Postal 70-256, Coyoacan, C.P. 04510, Mexico D.F
Parmiggiani F.:
ISAO-CNR, Via P. Gobetti 101-40129, Bologna, Italy
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