Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Hyperspectral Target Detection Method Based on Conditional Random Projection

A conditional random and target detection technology, applied in the field of remote sensing image processing, can solve the problems of unreasonable extraction and matching process, wide range of negative samples, etc., and achieve the effect of high test operation efficiency, improved sensitivity and short training time.

Active Publication Date: 2020-12-04
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the hyperspectral target spectral detection task, the target spectral data is clear, but the category of negative samples is too wide, and it is unreasonable for the deep learning model to treat the feature extraction and matching process of "positive / negative samples" indiscriminately

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
  • A Hyperspectral Target Detection Method Based on Conditional Random Projection
  • A Hyperspectral Target Detection Method Based on Conditional Random Projection
  • A Hyperspectral Target Detection Method Based on Conditional Random Projection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be described in detail below with reference to the accompanying drawings and examples. The flow chart of this embodiment is as follows figure 1 shown, including the following steps:

[0037] Step 1. Collect the spectral vectors of the images to be processed to form a training set and normalize them to obtain sample spectral data and their corresponding labels; the sample spectral data includes positive sample data and negative sample data;

[0038] The specific process is: for the image to be processed acquired by the same hyperspectral detector, the spectral vector corresponding to each pixel in the spatial dimension is extracted, and divided into spectral data including the target (positive sample) and the background (negative sample) and mark it. The marked spectral vectors are divided into training set and test set according to the required ratio, and the schematic diagram of the positive sample generative feature description and feature...

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 hyperspectral target detection method based on conditional random projection characteristics, which can realize accurate and rapid detection of a target and can improve the sensitivity to the number of training samples. The invention provides a supervised feature selection and feature extraction method based on conditional random projection in the aspect of characterization of hyperspectral image spectral data. A projection parameter matrix related to data and labels is obtained, and conditional random characteristics are obtained through the projection parameter matrix, so that the accuracy of target detection is improved; Meanwhile, screening estimation and sampling are added in the whole operation solving process; that the time for screening estimation and sampling operation is less, the training time is short, the screening estimation and sampling operation is completed in an offline stage, and a result obtained by training is directly used during actual operation, so that the method has the advantages of high detection accuracy and high test operation efficiency.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a hyperspectral remote sensing target rapid detection method based on a conditional random projection feature extraction strategy. Background technique [0002] With the rapid development of hyperspectral remote sensing technology, hyperspectral applications in military affairs and people's livelihood are becoming more and more extensive, which leads to higher and higher requirements for hyperspectral data processing for specific application environments. Among them, the detection of typical ground objects and targets in hyperspectral remote sensing images is an important research direction, which is of great significance in the scientificity of urban configuration, agricultural planting distribution planning, and military sensitive target classification and extraction. [0003] At present, in the field of typical target detection of hyperspectral objects, t...

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 Patents(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 唐林波周士超邓宸伟王文正赵保军
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com