Synthetic aperture radar image self-adaptive spot noise suppressing method

A technology of synthetic aperture radar and speckle noise, which is applied in the field of image processing, can solve the problems that the edge details and texture information cannot be well maintained, the image spatial resolution is sacrificed, and it is not applicable.

Inactive Publication Date: 2008-02-20
BEIHANG UNIV
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Problems solved by technology

[0003] Since SAR works based on the coherent imaging mechanism, there is serious speckle noise in the SAR image, which has a great impact on the interpretation and information extraction of radar images. Early SAR image speckle noise suppression was achieved through multi-view processing. , the main disadvantage of this method is that the spatial resolution of the image is sacrificed, and it is not suitable for the current high-resolution SAR system
[0004] At present, the spatial filtering method is usually used to suppress speckle noise. The spatial filtering method generally uses a sliding window to weight the pixels in the window to obtain the pixel value of the center point of the window. Traditional methods, such as Lee filtering, Kuan Filtering, Frost filtering and Gamma MAP filte

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[0036] The present invention will be described in further detail below in conjunction with accompanying drawing

[0037] The present invention proposes a synthetic aperture radar image self-adaptive speckle noise suppression method. In the method, a new quantitative index representing the local heterogeneity of SAR images—heterogeneity entropy is proposed. By realizing local heterogeneity The calculation method and processing flow of sex entropy, using the method of heterogeneity entropy measurement, to judge whether there is texture information or edge details in the local image, so it can more accurately judge the local homogeneity and heterogeneity of the image and automatically Adaptively adopt different noise reduction processing measures, effectively smooth speckle noise, and have a good effect of maintaining edge details and texture information

[0038] The present invention proposes a new quantitative index for characterizing the local heterogeneity of SAR images—the c...

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Abstract

The utility model discloses a silencing method of synthetic aperture radar image self adaptive speckle, providing a standard utilizing a global statistic heterogeneity entropy He to judge regional homogeneous or heterogeneous. Because the heterogeneity entropy He can utilize the global probability and statistics distribution of the image, the changes of edge detail and texture information can be measured accurately. And then the local homogeneous or heterogeneous of the image is judged more accurately and self adaptively utilizes different noise reduction measures. The method can inhibit the speckle noises of synthetic aperture radar image without sacrificing the spatial resolution, and reserve the role of the fringe goals texture edge details.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a speckle noise suppression method, in particular to an adaptive speckle noise suppression method for synthetic aperture radar images Background technique [0002] Synthetic Aperture Radar (SAR) is not limited by factors such as weather, geography and time. It can perform high-resolution imaging on the ground and has a certain penetrating power. Therefore, it is widely used in military reconnaissance, resource detection, fire, ocean observation, environmental monitoring, etc. [0003] Since SAR works based on the coherent imaging mechanism, there is serious speckle noise in the SAR image, which has a great impact on the interpretation and information extraction of radar images. Early SAR image speckle noise suppression was achieved through multi-view processing. , the main disadvantage of this method is that the spatial resolution of the image is sacrificed, and it is not suitable f...

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Application Information

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IPC IPC(8): G01S13/90G01S7/02G06T5/00
Inventor 陈杰朱晶刘滕冲周荫清李春升
Owner BEIHANG UNIV
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