Remote sensing image smoke detection method based on feature fusion

A remote sensing image and feature fusion technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as ignoring local features

Pending Publication Date: 2021-12-07
SOUTHEAST UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Many existing convolutional neural networks focus on global features based on repeated textures, while ignoring local features in images

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
  • Remote sensing image smoke detection method based on feature fusion
  • Remote sensing image smoke detection method based on feature fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0029] Such as figure 1 and figure 2 As shown, the present invention discloses a method for detecting smoke in remote sensing images based on feature fusion, the method comprising the following steps:

[0030] Step 1: Divide the remote sensing smoke detection data set into training set, verification set and test set. This remote sensing smoke detection data set contains a total of 6225 pictures of 6 types of scenes. The 6 types of scenes are: smoke, dust, haze, cloud, land and coast. Among them, the three scenes of dust, haze and cloud are very similar to smoke, and are used to let the neural network learn the characteristics of smoke from similar scenes. After d...

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 discloses a feature fusion-based remote sensing image smoke detection method. A feature fusion-based convolutional neural network provided by the invention mainly comprises three parts: a backbone network, a global feature branch and a local feature branch, wherein the backbone network is used as a preliminary feature extractor; the global feature branch is used for extracting global features such as repeated textures; the local branches are used for extracting significant features in the local key area; smoke in the remote sensing image has different shapes, textures and ranges, and smoke detection is facilitated by considering global features and local features. According to the remote sensing smoke detection method disclosed by the invention, the highest detection precision 96.22% is obtained on a public remote sensing data set.

Description

technical field [0001] The invention belongs to the field of video image processing, and in particular relates to a method for detecting smoke in remote sensing images based on feature fusion. Background technique [0002] Fire is a common natural disaster, which will cause great harm to human safety and property. It is very important to fire detection and alarm. When a fire occurs, the smoke captured by the satellite is used as a signal of the fire. With the increasing availability and performance of satellite remote sensing technology, the use of remote sensing images to detect smoke for fire detection has been widely adopted. However, the shape, texture, and color of smoke in remote sensing images vary greatly. Moreover, some scenes in remote sensing data, such as clouds and haze, are very similar to smoke, which increases the difficulty of smoke detection. Therefore, the research on remote sensing smoke detection is of great significance. [0003] With the developmen...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/253G06F18/214
Inventor 路小波陈诗坤曹毅超
Owner SOUTHEAST UNIV
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