Method for automatically detecting remote sensing ground object target based on stochastic geometry model

A random geometry, automatic detection technology, applied in computer parts, character and pattern recognition, instruments, etc., can solve the problems of complex structure and single characteristics of geometric parts

Inactive Publication Date: 2013-07-24
INST OF ELECTRONICS CHINESE ACAD OF SCI
View PDF4 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for automatic detection of remote sensing ground object targets based on random geometric models to solve the prob...

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
  • Method for automatically detecting remote sensing ground object target based on stochastic geometry model
  • Method for automatically detecting remote sensing ground object target based on stochastic geometry model
  • Method for automatically detecting remote sensing ground object target based on stochastic geometry model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0088] The present invention will be further described below in conjunction with the accompanying drawings.

[0089] figure 1 It is a schematic flow chart of the multi-class complex target recognition method based on the self-learning of multi-class primitives in the present invention, and the specific steps include:

[0090] The first step is to establish a representative image set of remote sensing ground objects. the way is:

[0091] 1.1. According to the needs, define three types of remote sensing ground objects with relatively complex structures such as aircrafts, ships, and buildings, but with relatively single geometric component characteristics;

[0092] 1.2. For each target category, select 100 images as representative images of this type of target, for each type of target image, select 40 images as a training set, and the remaining 60 images as a test set;

[0093] 1.3. For each target image, mark the category to which the target belongs and the area where it is l...

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 provides a method for automatically detecting a remote sensing ground object target based on a stochastic geometry model. The method solves the automatic detection problem of a target with a relatively complex structure but relatively singular geometric component features in a remote sensing image. The method comprises the following steps: establishing a plurality of classes of image representative sets comprising the remote sensing ground object target; constructing the stochastic geometry model aiming at a target to be processed by taking geometric components for forming the target as processing units; after constructing the stochastic geometry model of the target components, converting the automatic detection problem of the target into an optimal configuration problem of a stochastic target seeking process; estimating the maximum value of the non-parameter probability density by using a Markov chain Monte Carlo method; and finally, detecting the target by using the stochastic geometry model, judging whether the target exists in the tested image or not, ending and outputting a result that no target exists if no target exists, and processing the image by using the stochastic geometry model to obtain the detection result corresponding to optimal configuration and outputting the final detection position of the target if the target exists.

Description

technical field [0001] The invention relates to a method for target detection in the technical field of image information processing, in particular to a method for automatically detecting ground objects and targets in remote sensing scene images based on a random geometric model. Background technique [0002] According to the characteristics of remote sensing surface objects in terms of shape and appearance, it can be divided into a large category of targets with complex structures. The structure of this type of targets is relatively complex but the characteristics of geometric components are relatively simple, such as aircraft and ship targets. Due to the rich information contained in the remote sensing image and the complex scene, not only the detailed features of the target are enlarged, but also the interference is enhanced, which brings great difficulty to the detection and positioning of this type of target. [0003] Stochastic geometry theory is a modern random set th...

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
IPC IPC(8): G06K9/00G06K9/32
Inventor 孙显付琨王宏琦
Owner INST OF ELECTRONICS CHINESE ACAD OF SCI
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