SAR image change detection method based on maximum edge metric learning

A technology of image change detection and metric learning, which is applied in the field of radar technology remote sensing image processing, can solve the problem of low time complexity, achieve the effects of suppressing noise, solving boundary misclassification, and improving classification accuracy

Active Publication Date: 2018-11-20
XIDIAN UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0006] Aiming at the deficiencies of traditional change detection methods and traditional measurement methods, the present invention proposes a SAR image change detection method based on maximum edge metric learning that still maintains high precision with low time complexity

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  • SAR image change detection method based on maximum edge metric learning
  • SAR image change detection method based on maximum edge metric learning
  • SAR image change detection method based on maximum edge metric learning

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Embodiment 1

[0045] Compared with optical images, SAR images have their unique advantages: because SAR images are active imaging, SAR has broken through the limitations of optical remote sensing of external conditions such as weather, and has all-weather and all-day working capabilities, and contains phase A variety of information such as , amplitude and polarization make up for the lack of optical images. Therefore, SAR images are widely used, and SAR image change detection is one of its important applications.

[0046] SAR image change detection can be applied to natural disaster assessment. Compared with other imaging, SAR image imaging will not be affected by external conditions such as weather, and images with high imaging quality can still be obtained even under severe conditions. For example, two SAR images before and after the earthquake are obtained, and the degree of disaster after the disaster is observed according to the change detection of the SAR image, which is used to bette...

Embodiment 2

[0058] The SAR image change detection method based on maximum edge metric learning is the same as embodiment 1. In step (4), the positive and negative constraints are given labels, and the specific distribution method is as follows:

[0059] 4.1) If (x 1i ,x 2i )∈S, assign a label y i = 1;

[0060] 4.2) If (x 1i ,x 2i )∈D, assign a label y i =-1.

[0061] The present invention uses 1 and -1 instead of the traditional 1 and 0 when assigning labels, mainly for the positive and negative constraint pairs to work in the optimization process of metric learning. If the negative constraint pair is assigned a label of 0, it will result in The measure of the difference of negative constraints will not affect the optimization of the model, and the distance is always 0.

Embodiment 3

[0063] The SAR image change detection method based on maximum edge metric learning is the same as embodiment 1-2. In step (5), positive and negative constraints are used as input to set up a structured support vector machine model, which specifically includes the following steps:

[0064] The purpose of establishing the metric learning optimization form in the present invention is to find a positive semi-definite matrix A and the corresponding distance threshold b. For the threshold b, after adding a slack variable, for the constraint pair (x 1i ,x 2i )∈S, then the distance between them is less than the threshold b; for the constraint pair (x 1i ,x 2i )∈D, then the distance between them is greater than the threshold b. If the constraint pair S and D are regarded as two categories, then this problem can be transformed into a two-category problem. Because the structured support vector machine model has the minimum generalization error, so the present invention adopts the str...

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Abstract

The invention provides a SAR image change detection method based on maximum edge metric learning, which solves the problem that the SAR image change detection is susceptible to speckle noise and the traditional metric cannot measure the sample difference information well, and has the implementation steps comprising: constructing all samples by inputting the remote sensing image before and after the change; constructing training samples containing all the boundaries; constructing positive and negative constraint pairs using the training samples; by using the positive and negative constraint pairs as input, establishing a structured support vector machine model to obtain a mapping matrix, and decomposing the mapping matrix; mapping all the samples to the feature space by using the decomposedmapping matrix, and performing SAR image change detection classification on all the samples in the feature space. The invention is highs in classification accuracy, and still maintains a high-precision classification effect especially in the case of low time complexity, which maintains good boundary information while suppressing noise. The invention is used for SAR image change detection.

Description

technical field [0001] The invention belongs to the technical field of radar technology remote sensing image processing, and further relates to classification and recognition of remote sensing images, in particular to a SAR image change detection method based on maximum edge metric learning. It is used in disaster assessment, urban development, etc. Background technique [0002] Compared with other imaging methods, the imaging technology of Synthetic Aperture Radar (SAR) has its unique advantages. It can obtain detailed ground object information, and SAR will not be interfered by external factors such as weather conditions and time periods. At the same time, synthetic aperture radar uses synthetic aperture technology to improve its azimuth resolution, and uses pulse compression technology to improve its range resolution, so it can obtain large-area high-resolution remote sensing images, which provides a broad prospect for the application of SAR images. [0003] The existing...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06K9/40
CPCG06V20/13G06V10/30G06F18/214G06F18/243
Inventor 王蓉芳王玉乐陈佳伟焦李成冯婕刘红英尚荣华
Owner XIDIAN UNIV
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