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Remote sensing scene classification method based on semantic perception and dynamic graph convolution and system thereof

A scene classification and dynamic graph technology, applied in the field of image processing, can solve the problem of single training label

Pending Publication Date: 2022-01-28
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a remote sensing scene classification method and system based on semantic perception and dynamic image convolution to solve the problem that traditional DCNN is difficult to classify remote sensing images due to the single training label. Reasonable use of various semantic information to improve classification accuracy

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  • Remote sensing scene classification method based on semantic perception and dynamic graph convolution and system thereof
  • Remote sensing scene classification method based on semantic perception and dynamic graph convolution and system thereof

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Embodiment Construction

[0052] 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.

[0053] In the description of the present invention, it should be understood that the terms "comprising" and "comprising" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or more other features, Presence or addition of wholes, steps, operations, elements, components and / or collections thereof.

[0054] It should also be understood that the terminology used in the descriptio...

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Abstract

The invention discloses a remote sensing scene classification method based on semantic perception and dynamic graph convolution and a system thereof, and the method comprises the steps: constructing a feature pyramid based on a deep residual network to complete the preliminary extraction of features, and obtaining a feature F; constructing an adaptive semantic recognition module, and obtaining a region index I of the feature F through semantic recognition; according to the feature F, constructing a graph node by using the region index I and the feature F, and acquiring a global feature F* based on a feature information capture network of dynamic graph convolution; and realizing scene classification of the remote sensing image by using the global feature F*. The method and the system have more stable and accurate classification performance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing scene classification method and system based on semantic perception and dynamic graph convolution. Background technique [0002] With the improvement of the resolution of remote sensing images, high-resolution remote sensing images can display more detailed land cover information. According to different land cover semantics, HRRS images can be divided into different scenes. In recent years, scene classification of HRRS images has become more and more important, because it can be applied to many remote sensing image applications, such as urban and rural planning, land surface detection, etc. However, HRRS images have the characteristics of complex content, diverse semantics, multiple target scales, and large volume. These features make HRRS scene classification a difficult and challenging task. Therefore, how to improve the classification a...

Claims

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Application Information

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IPC IPC(8): G06V20/13G06V10/44G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/045G06F18/2415G06F18/241
Inventor 唐旭杨钰群马晶晶张向荣焦李成
Owner XIDIAN UNIV
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