Probabilistic Mapping Method of SAR Image Water Body Combined with Prior Probability Estimation

Abstract

The invention discloses a SAR image water body probability mapping method combined with prior probability estimation, comprising the following steps: step 1, establishing a statistical model assumption for the distribution of backscatter coefficients of SAR image pixels; step 2, estimating the prior probability of water body distribution; Step 3, according to the backscattering coefficient σ of the image in the study area 0 Estimate the distribution parameters; step 4, calculate the conditional probability that the pixel belongs to the water body. The present invention uses Bayesian inference to make a Gauss distribution assumption on the backscattering coefficient of the image pixel in the research area, and then combines the k-means clustering algorithm to classify the pixel into water body and non-water body, and calculates the proportion of water body pixels in the research area as the water body Finally, combined with the estimated value of the prior probability, the theoretical probability density function of the backscatter coefficient is superimposed on the statistical distribution histogram, and the nonlinear least squares fitting is used to complete the model parameter estimation, and the research area is obtained The probability that each pixel of the image belongs to a water body.

Description

technical field [0001] The invention relates to water body remote sensing extraction technology, in particular to a SAR image water body probability mapping method combined with prior probability estimation. Background technique [0002] Water body extraction from remote sensing images is a typical application of remote sensing in the water conservancy industry. SAR (synthetic aperture radar) data is suitable for the extraction and monitoring of flood disaster information due to its all-weather and all-time imaging characteristics, and plays an important role in the field of water conservancy remote sensing. The process of water body extraction can be attributed to the process of classifying ground objects and segmenting images. The classic remote sensing image water body extraction methods include threshold segmentation, classifier segmentation combining multiple features, segmentation based on level set theory, image segmentation based on energy function, etc. When the e...

AI Technical Summary

Problems solved by technology

However, in the case of not relying on other auxiliary information other than images, the existing probabilistic mapping methods simply choose to set the prior probability to the default value of 0.5, which will make the statistical model of the estimated ground object backscatter coefficient insufficient Accurate, reducing the precision of probability mapping results

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