SAR image change detection method based on priori, fusion gray level and textural feature

Abstract

The invention discloses a SAR image change detection method based on a priori, a fusion gray level and a textural feature. By using the method of the invention, problems that a Gaussian model can not completely fit distribution of a difference graph and change detection accuracy is low because only pixel gray level information of the SAR image is used are mainly solved. The method comprises the following realization steps that (1) two time phase SAR images which are registered and corrected are read in; (2) a wavelet fusion strategy is performed on the two images so as to construct the difference graph; (3) a classified priori probability of the difference graph is calculated; (4) the gray level of the difference graph and the texture information are fused so as to acquire an observed quantity likelihood probability; (5) the classified priori probability and the observed quantity likelihood probability are used to calculate a posteriori probability; (6) a maximum posteriori probability criterion is used to divide the difference graph into a change type and a non-change type; (7) a step (3) to a step (6) are repeated till a terminal condition is satisfied and a final change detection result is output. The method of the invention has the advantage that change detection precision to the SAR image is high. The method can be used to extract and acquire change detail information of the SAR image.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for detecting changes in images, in particular to a method for detecting changes in SAR images that have been registered in the same area at different times, which can be used to extract multi-temporal SAR images and obtain changes in ground objects Information, improve the accuracy of SAR image change detection, so as to carry out more accurate monitoring and evaluation of ground object information. Background technique [0002] With the rapid development of synthetic aperture radar (SAR) technology, its resolution has been continuously improved, and the obtained synthetic aperture radar image has the characteristics of not being affected by external weather conditions and sunlight intensity on ground object imaging, which makes up for the difference between optical sensors and infrared sensors. Due to the lack of imaging, the application of SAR images is increasing, and t...

AI Technical Summary

Problems solved by technology

The shortcomings of this method are: (1) The distribution of the difference map obtained in general is a mixed distribution, and the Gaussian model cannot fully fit the distribution of the difference map, and how to accurately infer each The mean, variance and shape parameters of the Gaussian components, as well as their weights, are also a complex statistical inference problem; (2) only the gray information of the difference map is used in the whole solution process, and other information of the difference map is not fully utilized, such as : Texture features and regional features, etc. Therefore, the specific speckle noise of SAR images will have a great impact on the detection results, increase the misclassification rate of pixels, and reduce the accuracy of change detection

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