A hyperspectral image classification method for multi-feature class sub-dictionary learning

A hyperspectral image and dictionary learning technology, which is applied in the field of hyperspectral image classification with multi-feature class sub-dictionary learning, and can solve the problems of different spectra of the same object and foreign objects of the same spectrum.

Inactive Publication Date: 2018-12-11
NANJING NORMAL UNIVERSITY
View PDF3 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a hyperspectral image classification method for multi-feature sub-diction

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 image classification method for multi-feature class sub-dictionary learning
  • A hyperspectral image classification method for multi-feature class sub-dictionary learning
  • A hyperspectral image classification method for multi-feature class sub-dictionary learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0044] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0045] Such as figure 1 As shown, the present invention provides a hyperspectral image classification method for multi-feature class sub-dictionary learning, comprising the following steps:

[0046] Step 1, extract spectrum, gradient, texture, and shape characteristic data of the hyperspectral image to be classified: use the existing technology to extract various characteristic information of the hyperspectrum, obtain sample data of different feature spaces, and pave the way for step 3. A variety of feature information is correlated and complementary, which provides more effective information for the correct classification of hyperspectral images, and further improves the classification accuracy.

[0047]Step 2, use the MFKCSDL (Multifeature Kernel Class Sub-dictionary Learning) model to learn the corresponding class sub-dictionary for eac...

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 hyperspectral image classification method for multi-feature class sub-dictionary learning, which comprises the following steps: extracting a plurality of complementary feature data of the hyperspectral image; using a MFKCSDL model to learn the corresponding class sub-dictionary for each training sample; dividing hyperspectral images into several spatial groups by watershed-based image segmentation; applying the class sub-dictionary combinations to the MFKJSR model to obtain the multi-feature representation coefficients of pixel points in each spatial group; predictingclass labels of pixels in the spatial group by minimizing the multi-feature reconstruction error of all pixels in the spatial group. The invention can effectively improve the discrimination ability of the dictionary and further improve the classification accuracy of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral image classification method for multi-feature class sub-dictionary learning. Background technique [0002] The problems of high dimensionality of hyperspectral image data, few training samples and high similarity between spectral bands have brought many challenges to its classification task. The classification model that only uses the spectral characteristics of a single pixel point is easily affected by factors such as "same object with different spectrum, same spectrum with different object", and the classification accuracy is low. With the deepening of research, researchers found that the spatial information describing the relationship between neighbors in hyperspectral images helps to further improve the classification accuracy, and then proposed a classification model that uses a large number of spectral information and spatial informatio...

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): G06K9/34G06K9/46
CPCG06V10/267G06V10/40G06V10/513
Inventor 杨明张会敏
Owner NANJING NORMAL UNIVERSITY
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