Polarimetric SAR image change detection method based on depth confidence network

A deep belief network and change detection technology, applied in the field of image processing, can solve the problems that it is difficult to obtain high classification accuracy for polarimetric SAR images, and does not consider the deep feature representation of polarimetric SAR images, so as to improve the accuracy of change detection

Active Publication Date: 2017-09-12
XIDIAN UNIV
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Problems solved by technology

[0005] Since these polarization SAR change detection methods do not take into account the deep feature representation of pol...

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  • Polarimetric SAR image change detection method based on depth confidence network
  • Polarimetric SAR image change detection method based on depth confidence network
  • Polarimetric SAR image change detection method based on depth confidence network

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Embodiment Construction

[0063] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0064] see figure 1 , the implementation steps of the polarization SAR image change detection method based on the deep belief network of the present invention are as follows:

[0065] Step 1, input two polarimetric SAR images of the same area with different phases to be detected;

[0066] Step 2, use ENVI software to register the polarization SAR data of the two time phases;

[0067] Step 3, use the refined Lee filter to reduce speckle on the registered image respectively;

[0068] Step 4: Preliminary manual marking of two polarimetric SAR images of the same area in different phases after registration and speckle reduction;

[0069] Step 5, obtain the polarization coherence matrices TA and TB from the polarization scattering matrix S of the two polarization SAR images respectively. In the case of backscattering, because the reciprocity has S HV = S VH = ...

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Abstract

The invention discloses a polarimetric SAR image change detection method based on a depth confidence network, and the method comprises the steps: inputting two to-be-detected polarimetric SAR images of the same region and different time phases; carrying out the registering of the polarimetric SAR images at two time phases; carrying out the despeckling of the registered images; carrying out the initial artificial marking; respectively solving polarization coherent matrixes TA and TB through the polarization scattering matrixes of the two polarimetric SAR images; respectively extracting diagonal elements of the matrixes, and carrying out the cascading to form a feature matrix F based on pixels; obtaining a feature matrix F1 after normalization; segmenting each element in the feature matrix F1 into a block, and forming a feature matrix F2 based on the image blocks; obtaining the feature matrix D1 of a training data set D and a feature matrix T1 of a test data set T according to F2; constructing a detection model based on a depth confidence network; carrying out the training of the detection model through the constructed data set; carrying out the detection of the to-be-detected images through the trained detection model. The method improves the detection precision.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a polarization SAR image change detection method based on a deep belief network. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-weather, all-time, high resolution, side-view imaging, etc., and can obtain richer information on targets. The method of polarimetric SAR image change detection is a method of comparing and analyzing polarimetric SAR imaging of the same place in different periods, and obtaining the change of ground object information in the same geographical location in different periods according to the difference between the information. Polarization SAR change detection has a wide range of applications in military and civilian fields. [0003] Compared with SAR images, polarimetric SAR images contain richer information, which can reveal the scattering mechanism of the tar...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/30G06T5/00G06T5/20
CPCG06T5/002G06T5/20G06T2207/10032G06T2207/20081G06T2207/20084G06T7/246G06T7/30
Inventor 焦李成屈嵘李玉景马晶晶杨淑媛侯彪马文萍刘芳尚荣华张向荣张丹唐旭
Owner XIDIAN UNIV
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