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

Supervised classification method of multi-class hyperspectrum remotely sensed data

A technology of hyperspectral remote sensing and supervised classification, which is applied in the field of multi-category supervised classification of hyperspectral remote sensing data, and can solve problems such as confusion of similar categories.

Inactive Publication Date: 2011-11-09
BEIHANG UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Normally, Logistic modeling needs to be achieved after reducing the dimensionality of hyperspectral data, otherwise it will bring a huge computational burden, but the data dimensionality reduction method will lose the detailed features of the spectrum while reducing the data dimension, resulting in similar categories confusion

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
  • Supervised classification method of multi-class hyperspectrum remotely sensed data
  • Supervised classification method of multi-class hyperspectrum remotely sensed data
  • Supervised classification method of multi-class hyperspectrum remotely sensed data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] In order to better illustrate the multi-category supervised classification method of hyperspectral remote sensing data involved in the present invention, PHI airborne hyperspectral data is used to carry out fine classification of crops in Fanglu tea farm area, Jiangsu. A multi-category supervised classification method for hyperspectral remote sensing data of the present invention, the specific implementation steps are as follows:

[0038] (1) Read in hyperspectral data: read in the hyperspectral data of the push-broom hyperspectral imager (PHI) in Fanglu Tea Farm, the data size is 210×150×64, and the band range is 455-805nm;

[0039] (2) Determine the number of classification categories, and select training samples: according to the reference image, the number of classification categories is J=6, and the training samples and test samples are obtained according to the reference image. The specific classification categories, training samples and test samples are shown in t...

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 relates to a supervised classification method of multi-class hyperspectrum remotely sensed data, which comprises the following steps: (1), reading the hyperspectrum data; (2), determining the class number, and selecting a training sample and a test sample; (3) computing multi-fractal spectrum parameters; (4), establishing a logarithm regression multi-class classification model basedon the multi-fractal spectrum parameters; (5) solving the model by using a maximum likelihood estimation method; (6) classifying by using a probability maximum principle and computing the classification precision. The invention does not need any assumption to the probability distribution of variables, and the number of the parameters to be estimated is less in the classifier model, the intra-class consistence is increased and the extra-class divisibility is improved by the multi-fractal characteristics, therefore, the method can obtain higher classification precision under the condition of less training samples.

Description

technical field [0001] The invention relates to a multi-category supervised classification method for hyperspectral remote sensing data, which belongs to the field of hyperspectral data processing methods and application technologies, and is suitable for research on theoretical methods and application technologies of hyperspectral data supervised classification. Background technique [0002] Supervised classification methods for hyperspectral remote sensing data mainly include two categories: methods based on spectral feature matching and methods based on statistical analysis models. Due to the influence of atmosphere, terrain, illumination and other conditions in the process of hyperspectral data acquisition, the spectral characteristics of the obtained ground objects vary greatly. Therefore, the method based on spectral feature matching will cause large confusion between different ground objects. Instability and other problems reduce the accuracy of the classification resu...

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): G06K9/66G01S7/48
Inventor 李娜赵慧洁贾国瑞牛志宇
Owner BEIHANG UNIV
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