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Remote sensing image change detection method based on neural network structure search

A remote sensing image and change detection technology, applied in the field of image processing, can solve the problem of low detection accuracy and achieve the effect of improving the change detection accuracy

Pending Publication Date: 2022-03-15
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a remote sensing image change detection method based on neural network structure search, which is used to solve the technical problem of low detection accuracy in the prior art

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  • Remote sensing image change detection method based on neural network structure search

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[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] refer to figure 1 , the present invention comprises the following steps:

[0049] Step 1) Obtain training sample set, verification sample set and test sample set:

[0050] (1a) Obtain T remote sensing images of size R×R at time A and B from the remote sensing image change detection data set HA={HA 1 ,···,HA t ,···,HA T}, HB={HB 1 ,···,HB t ,···,HB T}, and the label image HL of the area where HB changes relative to HA={HL 1 ,···,HL t ,···,HL T}, where, R≥256, T≥200, HA t 、HB t Indicates the tth remote sensing image at time A and time B, HL t means HB t relative to HA t label image of the changed area;

[0051] In this embodiment, the remote sensing images and label images at time A and B are obtained from the LEVIR-CD data set, R=1024, T=637;

[0052] (1b) Each remote sensing image HA at time A and time B t 、HB...

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Abstract

The invention provides a remote sensing image change detection method based on neural network structure search. The remote sensing image change detection method comprises the implementation steps of obtaining training, verification and test sample sets; constructing a super neural network model; performing iterative training on the super neural network model; searching the trained super-neural network model by adopting a genetic algorithm to obtain structure search parameters; constructing a remote sensing image change detection model based on structure search parameters; performing iterative training on the remote sensing image change detection model; and acquiring a change detection result of the remote sensing image. The genetic algorithm is adopted to search the trained super-network neural model, the structure of the remote sensing image change detection model is determined through the searched structure search parameters, the structure of the remote sensing image change detection model can be highly matched with the characteristics of the remote sensing image, the remote sensing image characteristics are extracted more sufficiently, and the detection accuracy is improved. And the change detection precision of the remote sensing image is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a remote sensing image change detection method, in particular to a remote sensing image change detection method based on neural network structure search, which can be used in the fields of geological disaster monitoring, land cover investigation, urban planning and the like. Background technique [0002] Remote sensing images are images obtained by collecting and processing the electromagnetic radiation energy of ground objects and targets by remote sensing imaging instruments on the remote sensing platform. The two remote sensing images obtained by shooting the same place at two different times can reflect the changes of ground objects. Remote sensing image change detection refers to the detection of areas that have changed between two remote sensing images taken at different times in the same place. How to improve the accuracy of change detection is the focus and diffic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/13G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/086G06N3/045G06F18/253G06F18/214
Inventor 李阳阳郭宣威吴彬陈茜焦李成尚荣华李玲玲马文萍
Owner XIDIAN UNIV
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