SAR (Synthetic Aperture Radar) image change detection method based on Memetic kernel clustering

An image change detection and image detection technology, which is applied in the field of image processing, can solve the problems of inaccurate edge positioning, affecting consistent edge performance, and long time consumption, so as to improve edge preservation, regional consistency and edge preservation performance. , Reduce the effect of low error checking rate

Inactive Publication Date: 2012-06-20
XIDIAN UNIV
View PDF2 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing optimization methods often take a long time to deal with optimization problems, and are easy to fall into local optimum during the search process. At the same time, for the change detection problem of complex images, there are often shortcomings of inaccurate edge positioning. Affects the performance of area consistency and edge preservation of image change detection

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SAR (Synthetic Aperture Radar) image change detection method based on Memetic kernel clustering
  • SAR (Synthetic Aperture Radar) image change detection method based on Memetic kernel clustering
  • SAR (Synthetic Aperture Radar) image change detection method based on Memetic kernel clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0029] Step 1: Filter the two SAR images at different times to be detected. Here, select the median filter in morphology and use this filter to filter the input two-temporal images to obtain the filtered image I 1 and I 2 .

[0030] Step 2: On the filtered image I 1 and I 2 , find the logarithmic ratio difference image I 3 , and will get the I 3 The gray value of is used as a clustering dataset.

[0031] (2a) Calculate the logarithmic ratio difference image I of the change detection two-temporal SAR image D :

[0032] I D =|log(I 2 +1)-log(I 1 +1)|;

[0033] (2b) Normalize the difference image to obtain the logarithmic ratio difference image map I 3 :

[0034] I 3 =255*(I D -I min ) / (I max -I min );

[0035] where I max =max(I D ) means I max The maximum gray value in the medium, I min =min(I D ) means I min Minimum grayscale value.

[0036] Step 3...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on Memetic kernel clustering; and the SAR image change detection method mainly solves the problems that the conventional algorithm is high in time complexity and easy to fall into a local optimum value, and has bad performance in region consistency and edge retaining. The implementation process of the SAR image change detection method comprises the steps of: (1) inputting two SAR images at different times, and performing median filtering on the two images; (2) computing the logarithmic ratio difference striograph of the two time-phase images subjected to change detection; (3) setting initial conditions; (4) carrying out kernel clustering and computing a fitness function fk; (5) selecting optimal individuals after performing clone and dual mutation operation on current individuals; (6) selecting optimal individuals after performing clone and crossover operation on the optimal individuals obtained in the step (5); (7) selecting a final individual by an elitist strategy; and (8) judging stop conditions, and outputting clustering results if the conditions are satisfied, otherwise returning to the step (4). The SAR image change detection method based on Memetic kernel clustering has the advantages of rapid convergence rate, high detection precision and accuracy in edge retaining, and can be applied to target identification and change detection of the images in the image processing field.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly optimizes the kernel clustering fitness function value based on a Memetic local learning algorithm. The method can be used for change detection of images collected at different times. Background technique [0002] Remote sensing image change detection is to study the changes between images of the same scene in different time periods. Image changes mainly detect changes in radiance values ​​and local textures. These changes may be caused by real changes in the image scene, or by changes in illumination angle, atmospheric conditions, sensor accuracy, ground humidity, etc. An important application of image data obtained from airborne and spaceborne SAR is the change detection. [0003] Change detection technology has been widely used in civilian fields such as environmental monitoring, such as land use analysis, forest harvesting monitoring, disaster estimation, etc. Since SAR...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/12
Inventor 李阳阳吴波焦李成缑水平刘若辰马文萍尚荣华于昕
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products