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A Method of Fusion and Denoising of Multiple Remote Sensing Images Based on DS Evidence Theory

A remote sensing image fusion and remote sensing image technology, applied in the field of remote sensing image denoising processing, to achieve the effect of good edge texture details and rich information sources

Active Publication Date: 2019-04-16
SHANGHAI OCEAN UNIV
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  • A Method of Fusion and Denoising of Multiple Remote Sensing Images Based on DS Evidence Theory
  • A Method of Fusion and Denoising of Multiple Remote Sensing Images Based on DS Evidence Theory
  • A Method of Fusion and Denoising of Multiple Remote Sensing Images Based on DS Evidence Theory

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Embodiment 1

[0035] According to the remote sensing images of different time series, due to the influence of different factors such as atmospheric radiation and temperature, the location and degree of noise pollution of the remote sensing images are also different, that is, the noise content of the remote sensing images in the same area at different times is different. A certain position of the image is noise, but the position of another image does not necessarily contain noise. The complementary information of multiple remote sensing images is used for fusion and denoising, and the data with better quality in multiple remote sensing images (non-noise data or noise data) are selected. Data attenuation data), fused into a new image.

[0036] Such as figure 1 As shown, a multiple remote sensing image fusion denoising method based on DS evidence theory, the method specifically includes the following steps: Step 1, select multiple remote sensing images at the same location for a certain period...

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Abstract

The invention relates to a multi-remote sensing image fusion denoising method based on a DS evidence theory. The method particularly comprises the following steps: multiple remote sensing images at the same position at a certain time period are selected, and four noise models are built for each image; statistical analysis of data is carried out on the four noise models, and the possibility that each pixel point under each mode is the noise is obtained as basic possibility distribution for the DS evidence theory; a DS evidence theory fusion rule is used for fusing four evidences into one evidence, and the possibility that each pixel point in each image is the noise is obtained; the DS evidence theory fusion rule is repeatedly used, information of the multiple remote sensing images is fused, and a total evidence for fusion of the multiple remote sensing images is obtained; and finally, according to the evidence, a confidence interval is calculated, a well-designed decision rule is used for denoising, and a fused and denoised image is obtained. The method of the invention has the advantages that with the help of rich information sources, edge texture details of the remote sensing image can be better kept while denoising is realized.

Description

technical field [0001] The invention relates to the technical field of remote sensing image denoising processing, in particular, it is a multiple remote sensing image fusion denoising method based on DS evidence theory. Background technique [0002] Digital images are easily polluted by noise in the process of acquisition and transmission. The contaminated image will affect the further processing of the image, and bring certain difficulties to the understanding and recognition of the image. Studies have shown that in an image, when the signal-to-noise ratio is lower than 14.2db, the probability of false detection in image segmentation is greater than 0.5%; in parameter estimation, the estimation error of parameters is greater than 0.6%; in the determination of the number of regions , it is easy to overestimate the number of regions. Therefore, before edge detection, parameter estimation, feature extraction, information analysis and pattern recognition are processed on the i...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10032
Inventor 黄冬梅朱贵鲜张明华徐首珏石少华王丽琳
Owner SHANGHAI OCEAN UNIV
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