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Heterogeneous image change detection method based on non-supervision depth neural network

A deep neural network and image change detection technology, applied in the field of remote sensing image processing, can solve problems such as complex implementation and inability to meet requirements, and achieve excellent feature learning ability, excellent effect, and stable results.

Active Publication Date: 2017-06-20
济宁西电人工智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of method is easy to understand, but it is more complicated to implement, and it cannot avoid the influence of atmospheric conditions and sensor noise on the detection results
Nowadays, with the rapid development of sensor technology and the continuous deepening of the application of change detection technology, the change detection of heterogeneous images has a broad application prospect. At the same time, the requirements for its accuracy are getting higher and higher, and the traditional methods can no longer meet the requirements.

Method used

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  • Heterogeneous image change detection method based on non-supervision depth neural network
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  • Heterogeneous image change detection method based on non-supervision depth neural network

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Embodiment 1

[0042] This embodiment provides a heterogeneous image change detection method based on an unsupervised deep neural network, such as figure 1 shown, including the following steps:

[0043] Step 1: Select two heterogeneous images of the same region with different phases, denoted as image I 1 and image I 2 , using a deep neural network to image I 1 The neighborhood information of all points is input, and the reconstructed image I 2 Neighborhood information, get the initial reconstruction mapping function f 1 (x), get the initial difference map DI 1 ;

[0044] Step 2: The initial difference map DI obtained in Step 1 1Select sample points in , retrain the deep neural network, and obtain the final reconstructed mapping function f(x);

[0045] Step 3: Use the final reconstruction mapping function f(x) obtained in Step 2 to obtain the difference map DI to obtain the final change detection result.

[0046] The present invention breaks through the traditional heterogeneous image...

Embodiment 2

[0048] This embodiment further describes step 1 in detail on the basis of embodiment 1, as figure 2 As shown, step one specifically includes the following steps:

[0049] Step 101: Select two heterogeneous images of the same region with different phases, denoted as image I 1 and image I 2 , take the position (i, j) pixel as the center pixel, take a window whose size is 5×5, and the total number of pixels is N=25, extract two images I 1 , I 2 Neighborhood information IF 1 , IF 2 ;

[0050] Step 102: Randomly initialize the deep neural network, initialize the iteration counter t=0, and the maximum number of iterations T=50;

[0051] Step 103: Convert the image I 1 Neighborhood information IF 1 Input point by point in the deep neural network of step 102, with image I 2 Neighborhood information IF 2 The corresponding points of are used as labels, and the network parameters are updated using the conjugate gradient algorithm based on the minimum cross-entropy, t=t+1;

[...

Embodiment 3

[0056] This embodiment further describes step 2 in detail on the basis of embodiment 1 and embodiment 2, as image 3 As shown, step two specifically includes the following steps:

[0057] Step 201: The initial difference map DI obtained in step 1 1 Perform threshold segmentation to obtain the initial detection result Iout 1 ;

[0058] Step 202: Select IF 1 , IF 2 The corresponding Iout in 1 The points that are 0 in form a new set IF 10 , IF 20 ;

[0059] Step 203: Randomly initialize the deep neural network, initialize the iteration counter t=0, and the maximum number of iterations T=50;

[0060] Step 204: set IF 10 Input point by point into the deep neural network of step 203, with IF 20 The corresponding points of are used as labels, and the network parameters are updated using the conjugate gradient algorithm based on the minimum cross-entropy, t=t+1;

[0061] Step 205: Repeat step 204 until the error is smaller than the specified threshold or t>T, and a new mapp...

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Abstract

The invention belongs to the technical field of remote sensing image processing, and specifically relates to a heterogeneous image change detection method based on a non-supervision depth neural network. The method comprises the following steps: carrying out the registration of two heterogeneous images of the same region at different phases; taking the neighbor information of all points of the image 1 as the input through the depth neural network; carrying out the reconstruction of the neighbor information of the image 2, obtaining an initial reconstruction mapping function, and obtaining an initial difference image; selecting sample points, retraining the depth neural network, and obtaining a final reconstruction mapping function; obtaining the difference image through the final reconstruction mapping function, and obtaining a final change detection result. The method is suitable for the change detection of heterogeneous images at first, avoids the preprocessing link of an original image, reduces the information loss to a certain degree, is small in noise impact, and is high in precision of a change detection result.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and specifically relates to a heterogeneous image change detection method based on an unsupervised deep neural network, which mainly solves the problem of remote sensing image change detection and realizes the detection of heterogeneous remote sensing image changes. Background technique [0002] With the development of remote sensing technology, change detection technology has become an important branch of remote sensing image application. In recent years, the change detection methods of remote sensing images have been continuously updated, and the technology has become increasingly mature, and has been widely used in industrial and agricultural production, scientific research, and military fields. Remote sensing image change detection technology is based on the data of two remote sensing images covering the same area in different periods, combined with the imaging mechani...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11
Inventor 公茂果马晶晶王志锐武越刘嘉李豪王善峰张普照
Owner 济宁西电人工智能科技有限公司
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