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Polarization SAR image road extraction method based on conditional random field

A conditional random field and road extraction technology, applied in the field of image processing, can solve problems such as difficulty in implementation, and achieve the effect of high efficiency, suitable for popularization and use, and good accuracy

Active Publication Date: 2017-05-24
WUHAN UNIV
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Problems solved by technology

However, MRF needs to estimate the joint distribution of labels and data, and involves the distribution of data, so it is always difficult to implement

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  • Polarization SAR image road extraction method based on conditional random field
  • Polarization SAR image road extraction method based on conditional random field
  • Polarization SAR image road extraction method based on conditional random field

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Embodiment Construction

[0029] Aiming at polarimetric SAR images, the present invention proposes a conditional random field-based polarimetric SAR image road extraction method by combining polarimetric features, multi-resolution information and statistical properties.

[0030] The technical solutions of the present invention will be described in detail below according to the accompanying drawings and embodiments.

[0031] The Conditional Random Field (CRF) model is a discriminative graph model. The model training and inference process uses the existing label likelihood and posterior probability distribution of the entire image, which has the advantage of achieving accurate and robust labeling results. The present invention is based on the conditional random field model. Implement road detection. The principles of the technical solutions provided by the embodiments of the present invention are as follows: figure 1 As shown, including the input SAR image, the multi-scale image pyramid is obtained by t...

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Abstract

The invention provides a polarization SAR image road extraction method based on a conditional random field. A conditional random field framework is built to realize road extraction. The method comprises the following steps: building a pyramid for an input SAR image through use of a multi-scale line feature detection MLFD operator to get a multi-scale image pyramid; carrying out modeling through CRF, and introducing context information; modeling a cell potential function through a logistic function; adopting a Beamlet decomposition mode to look for optimal partitioning according to potential functions in pair; using relevant constraints to group and mark road elements, describing potential functions in pair, and merging information obtained from global data; and normalizing characteristic matrixes in a unified manner. By using the technical scheme, road extraction is efficient and precise. The method is suitable for popularization.

Description

technical field [0001] The invention belongs to the technical field of image processing, and particularly relates to a method for extracting roads from polarimetric SAR images based on conditional random fields. Background technique [0002] Synthetic Aperture Radar (SAR) images have extremely strong real-world applications, such as mapping, remote sensing, urban planning, agriculture, and disaster prevention. Compared with the original single-polarization SAR, the polarimetric SAR shows the capability in the application of the data and is able to obtain richer target information, as well as the identification of the fully polarized light scattering mechanism. It adopts the principle of active imaging to achieve continuous tracking of targets around the clock without being affected by weather and lighting. Among the applications of SAR image interpretation, road extraction has very important research significance, because linear indicators (including roads, bridges, ridges,...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/182
Inventor 何楚刘新龙张芷
Owner WUHAN UNIV
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