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Multi-modal image registration method and system based on depth global features

A multi-modal image and global feature technology, applied in the field of image processing, can solve problems such as not considering the whole

Pending Publication Date: 2021-08-06
XIDIAN UNIV
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Problems solved by technology

The network consists of a feature extraction part (Siamese network) and a similarity measurement part (dot product layer), this method can generate accurate and reliable matching points between optical and SAR images, but for a specific area, without considering the global

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  • Multi-modal image registration method and system based on depth global features
  • Multi-modal image registration method and system based on depth global features
  • Multi-modal image registration method and system based on depth global features

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Embodiment Construction

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0063] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and / or collections thereof.

[0064] It should also be understood that the terminology used ...

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Abstract

The invention discloses a multi-modal image registration method and system based on depth global features. The method comprises the following steps: preparing a data set and making the data set; making a data set; preprocessing the image; designing a full-convolution twin network structure; extracting image features; obtaining a similarity score graph; based on the similarity score graph, using a comparison loss function to optimize the similarity score graph, using a peak value loss function to increase the convergence speed, and using a sorting loss function to enable similarity distribution of the positive samples to be close to real distribution; jointly optimizing the comparison loss, the sorting loss and the peak loss, loading the trained weight into the model, reading all test set data in sequence, and predicting translation parameters of a floating graph in a test set in a reference graph; and determining the position of the floating image on the reference image to realize multi-modal image matching. According to the method and system, a high-precision matching result can be obtained by deeply mining common characteristics of bottom layers of different-source images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically relates to a multi-modal image registration method and system based on deep global features, which can be used for target tracking, heterogeneous image registration, etc., and can effectively improve the matching accuracy of heterogeneous images. Background technique [0002] With the development of sensor technology, the types of remote sensing images are becoming more and more diverse, and there are more and more ways to obtain information. However, because different types of sensors have their own differences, and are affected by external conditions such as time and environment, the acquired images will have different degrees of differences and limitations. [0003] In order to solve the above problems, it is usually necessary to make full use of the images obtained by different sensors. Image registration is an important step in multimodal images and has been widely...

Claims

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Application Information

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IPC IPC(8): G06T7/33G06K9/62G06N3/04G06N3/08
CPCG06T7/33G06N3/08G06T2207/20081G06N3/045G06F18/22G06F18/214
Inventor 王爽雷睿琪李毅魏慧媛权豆杨博武段宝瑞焦李成
Owner XIDIAN UNIV
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