Hyperspectral image classification method based on spatial and spectral features and sparse representation

A hyperspectral image and spectral feature technology, applied in the field of hyperspectral image processing, can solve problems such as low classification accuracy and insufficient use of hyperspectral image spatial information

Inactive Publication Date: 2014-09-10
HARBIN ENG UNIV
View PDF1 Cites 30 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the existing classification methods consider the extraction and utilization of spectral information. The traditional hyperspectral data classification me

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
  • Hyperspectral image classification method based on spatial and spectral features and sparse representation
  • Hyperspectral image classification method based on spatial and spectral features and sparse representation
  • Hyperspectral image classification method based on spatial and spectral features and sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0067] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0068] to combine Figure 1-9 , the concrete steps of the present invention are as follows:

[0069] 1. Read in the hyperspectral image data.

[0070] Read in the three-dimensional hyperspectral high-dimensional data, convert it from three-dimensional to two-dimensional data to facilitate subsequent processing, and normalize the obtained two-dimensional data to determine the sample category to be processed The number is L.

[0071] 2. Extract image features.

[0072] Extract the spatial texture features and spectral features of the image respectively, wherein step 2.1 and step 2.2 are performed in parallel.

[0073] 2.1 Extract image space texture features.

[0074] (1) Perform PCA transformation on the image. Subsequent steps are carried out on the basis of the first principal component grayscale image obtained after PCA transformation.

[0075] (2) Perform ...

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 aims at providing a hyperspectral image classification method based on spatial and spectral features and sparse representation. The method comprises the following steps that: hyperspectral high-dimension data is read, dimension conversion is carried out, and the hyperspectral high-dimension data is subjected to normalization processing, wherein the contained sample class number is L; the image spatial vein features and the spectral features are respectively extracted to obtain the spatial vein features T1 and the spectral features T2; a spatial and spectral feature set T of images is obtained through merging the spatial vein features and the spectral features, wherein T={T1, T2}; a part of samples is respectively selected from T for L classes to form a training set A, and a test set is set to a state that the set of all of the L classes in the T is M; the M and A are utilized for solving a sparse representation coefficient S<^> of the hyperspectral data for image reconstruction, and in addition, corresponding redundancy in each class is calculated; and the classes of the samples are determined according to the redundancy. The hyperspectral image classification method has the advantages that the information in hyperspectral images can be sufficiently utilized, and the hyperspectral images can be perfectly depicted through the spatial and spectral features; the classification precision can be improved; and the method can be applicable to different hyperspectral images, and the applicability is high.

Description

technical field [0001] The invention relates to an image processing method, specifically a hyperspectral image processing method. Background technique [0002] Hyperspectral images have very high spectral resolution, can obtain very narrow band intervals and a large number of spectral bands, so as to obtain image data containing rich information and continuous spectra, so they are widely used in agricultural production, mineral mapping, Target recognition and detection, disaster early warning and urban planning and other fields. Due to the large amount of data, high redundancy, high dimensionality, and strong correlation between bands are the main characteristics of hyperspectral data, which will bring great challenges to subsequent processing. [0003] Classification technology occupies a very important position in the process of hyperspectral image processing, which mainly divides the pixels with similar properties or characteristics in the data into the same class. Most...

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): G06K9/62G06K9/46
Inventor 王立国杨京辉窦峥赵春晖
Owner HARBIN ENG 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