Treelet curvelet domain denoising- based method for change detection of remote sensing image

Abstract

The invention discloses a Treelet curvelet domain denoising- based method for change detection of a remote sensing image. The method comprises the following steps: (1), reading data; (2), carrying out median filtering; (3), constructing a logarithm difference image; (4), constructing an absolute value difference image; (5), carrying out fast discrete curvelet decomposition; (6), carrying out classification on curvelet transform coefficients; (7), assigning all the curvelet transform coefficients of a Fine scale layer a value of zero ; (8), carrying out denoising on a Detail scale layer; (9), carrying out curvelet transform; (10), calculating a change proportion threshold; (11), carrying out classification; and (12) obtaining a change detection result graph. According to the invention, themethod has good robustness on noises; marginal information of a change area can be well maintained and fake change information is reduced; and the method has a high detection precision; moreover, themethod can be applied to fields including disaster monitoring, forest coverage rate assessment and city planning and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a remote sensing image change detection method based on Treelet curvelet domain denoising. This method can be applied to disaster monitoring and assessment in natural disasters, forest coverage monitoring and assessment, urban planning and other fields. Background technique [0002] Change detection is the process of identifying differences in state by observing an object or phenomenon at different times. With the development of remote sensing technology and information technology, remote sensing image change detection has become an important research direction of remote sensing image analysis. [0003] Remote sensing image change detection methods can be divided into two categories: first classification and then comparison method and first comparison and then classification method. The advantage of the method of classification first and then comparison is that i...

AI Technical Summary

Problems solved by technology

Although this method can perform further type analysis on the change region class, and obtain the change region weakening class and the change region enhancement class, there is still a shortcoming that this method only considers the low frequency information of the image when performing reliable scale feature level fusion , so the edge information of the changing area cannot be well maintained

In addition, because the method uses the EM algorithm for classification, there are more pseudo-change information in the detection results, which affects the accuracy of remote sensing image change detection.

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