Remote sensing image area segmentation method integrating Markov random field (MRF) and Bayesian network (BN)

Abstract

The present invention discloses a remote sensing image area segmentation method integrating an MRF and a Bayesian network, mainly for solving the problem that a conventional MRF method can not describe the directed information effectively. The method comprises the steps of firstly segmenting a remote sensing image, then dividing the areas, the boundaries, the vertexes, the semantic features and the relationships among the features which are extracted from the image into undirected and directed two forms, and then modeling the undirected relationships, such as the spatial mutual influence of the neighborhood pixels labels, etc., by a typical undirected graphical model-MRF, and modeling the directed relationships that the two edges of the boundary do not belong to the same kind generally, the vertexes are the cross points of two or more boundaries, etc., by the Bayesian network, thereby overcoming the disadvantage that the single layer MRF is not convenient to describe the directed relationships. Finally, the MRF and the BN are integrated by the data assimilation thought in the meteorological field, thereby improving the segmentation effect. A segmentation result obtained by the present invention possesses the better precision and area consistency, and can be used for the segmentation of a high resolution remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a remote sensing image area segmentation method integrating MRF and Bayesian network. Background technique [0002] In recent years, with the continuous advancement of aerospace, computer, communication and data acquisition technologies, high-resolution remote sensing technology has developed rapidly. High-resolution remote sensing images have a wide range of application values ​​in forest resources investigation, ecological environment monitoring, urban planning, mineral prospecting, precision agriculture, natural disaster monitoring and other fields. my country is rich in forest resources, and many places are prone to fires. Using remote sensing images to monitor fire-prone areas can reduce losses caused by fires; my country is also a large agricultural country, and remote sensing images can also be used to monitor the growth status of crops and changes in soil moisture....

AI Technical Summary

Problems solved by technology

[0012] The object of the present invention is to overcome the defect that above-mentioned technology exists, provide a kind of remote sensing image area segmentation method of integrated MRF and Bayesian network, adopt MRF and BN to describe the undirected information and directed information that are used for segmentation respectively, then MRF Integrating with BN, first divide the relationship between regions, boundaries, vertices, semantic features and features extracted from the image into two forms: undirected and direc

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