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
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • 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

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

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0041] Example 1:

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

[0043] Step 1: Select two heterogeneous images of the same area in different time phases and mark them as image I 1 And image I 2 , Using deep neural networks to image I 1 The neighborhood information of all points is input, reconstructed image I 2 The neighborhood information of, 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 1 Select sample points in, retrain the deep neural network, and get the final reconstruction 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, and obtain the final change detection result.

[0046] The present invention breaks through the traditional heterogen...

Example Embodiment

[0047] Example 2:

[0048] This embodiment further describes step one 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 area in different time phases, and mark them as image I 1 And image I 2 , Take the pixel at position (i, j) as the central pixel, take a window with a size of 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: Initialize the deep neural network randomly, initialize the iteration counter t=0, and the maximum number of iterations T=50;

[0051] Step 103: The image I 1 Neighborhood information IF 1 Input point by point into the deep neural network of step 102, and take the image I 2 Neighborhood information IF 2 The corresponding point of is used as the label, and the network parameters are updated using the conjugate gradient algorithm based on the minimum cross en...

Example Embodiment

[0055] Example 3:

[0056] This embodiment further describes step two 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: Convert the initial difference map DI obtained in step 1 1 Perform threshold segmentation to get the initial detection result Iout 1 ;

[0058] Step 202: Select IF 1 , IF 2 Corresponding Iout in 1 Points with 0 in the middle form a new set IF 10 , IF 20 ;

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

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

[0061] Step 205: Step 204 is continuously repeated until the error is less than a prescribed thresh...

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 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

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
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
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap