Remote sensing image change detection method based on saliency detection and deep twin neural network

A remote sensing image and change detection technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as the application of dual-window deep twinning networks, unpublished academic papers, etc., and improve feature extraction and expression capabilities. , Reduce salt and pepper noise, high efficiency

Active Publication Date: 2020-04-07
EAST CHINA NORMAL UNIV
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Problems solved by technology

[0005] The change detection of the existing technology has not yet applied the dual-window deep Siamese network to the field of change d

Method used

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  • Remote sensing image change detection method based on saliency detection and deep twin neural network
  • Remote sensing image change detection method based on saliency detection and deep twin neural network
  • Remote sensing image change detection method based on saliency detection and deep twin neural network

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Embodiment 1

[0043] See attached figure 1 , the specific implementation steps of the present invention are as follows:

[0044] Step 1: Perform image preprocessing on the two-temporal remote sensing images. The preprocessing process includes steps such as radiometric calibration, atmospheric correction, and geometric correction. The specific implementation process is as follows:

[0045] 1) In order to ensure that the same pixel in the multi-temporal remote sensing image corresponds to the same geographic location, it is necessary to perform relative registration on the two-temporal remote sensing image. RMSerror) was controlled within 0.5 pixels. In both regions, the T1 phase image was used as the reference image for the experiment, and the T2 phase image was used as the image to be registered. The geometric correction uses a quadratic polynomial model, and the nearest neighbor interpolation method is used in the resampling process.

[0046] 2) In order to eliminate the difference in ra...

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Abstract

The invention provides a remote sensing image change detection method based on significance detection and a deep twin neural network. The remote sensing image change detection method is characterizedby comprising the following steps: preprocessing a two-time-phase remote sensing image; carrying out normalization processing on the difference image; carrying out multi-scale segmentation and mergingoptimization; obtaining a saliency detection graph; establishing a double-window deep twin convolutional network model and training the double-window deep twin convolutional network model; and fusingthe segmentation object and the pixel-level change detection result through judgment to finally obtain a change detection result graph. Deep learning is successfully applied to the field of change detection, salt and pepper noise existing in high-resolution image change detection is reduced, and the precision of change type detection is effectively improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing data processing, in particular to a remote sensing image change detection method based on saliency detection and deep twin neural network. Background technique [0002] Change detection is to analyze and determine the characteristics and process of surface changes from remote sensing data in different periods, and remote sensing earth observation technology has the characteristics of wide range, long time and periodic observation, so change detection based on multi-temporal remote sensing images has been It is widely used in various fields such as urban expansion planning, vegetation coverage, and land use type monitoring. With the increasing abundance of high-resolution remote sensing image resources, the amount of data to be processed is also increasing rapidly, and the requirements for computer configuration are also getting higher and higher. Therefore, the research and exploration of c...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/464G06N3/045Y02T10/40
Inventor 谭琨王默杨王雪杜培军丁建伟
Owner EAST CHINA NORMAL UNIV
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