Variable class remote sensing image segmentation method based on optimal fuzzy factor selection

A fuzzy factor and remote sensing image technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as poor anti-noise performance and inability to achieve accurate image segmentation

Inactive Publication Date: 2017-02-15
LIAONING TECHNICAL UNIVERSITY
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Among them, the classic clustering method has the iterative self-organizing data analysis technique method (ISODATA), but this method

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
  • Variable class remote sensing image segmentation method based on optimal fuzzy factor selection
  • Variable class remote sensing image segmentation method based on optimal fuzzy factor selection
  • Variable class remote sensing image segmentation method based on optimal fuzzy factor selection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0040] A variable class remote sensing image segmentation method based on optimal fuzzy factor selection, such as figure 1 shown, including the following steps:

[0041] Step 1: read the remote sensing image to be segmented, measure the spectral measurement vector of each pixel in the remote sensing image to be segmented, and represent the remote sensing image to be segmented as a set of spectral measurement vectors of each pixel;

[0042] In this embodiment, the remote sensing image to be segmented is defined as X={x i , i=1,...,n}, where i is the pixel index, n is the number of pixels, x i =(x i1 ,...,x id ) is the spectral measure vector of pixel i, and d is the nu...

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 variable class remote sensing image segmentation method based on optimal fuzzy factor selection. The method comprises: reading a to-be-segmented remote sensing image; determining an optimal homogeneous region class number of the to-be-segmented remote sensing image; finding the homogeneous region class of each pixel spectrum measurement vector in the to-be-segmented remote sensing image through de-fuzzification, and obtaining a segmentation result of the to-be-segmented remote sensing image. The method adopts the partition entropy index as the index of a preferable fuzzy factor. When the fuzzy factor of the to-be-segmented remote sensing image is smaller than the optimal fuzzy factor, a PE index is larger. When the fuzzy factor is just equal to the optimal fuzzy factor, the PE index jumps to a smaller value, and the PE index becomes stable gradually when the number of fuzzy factors further increases. The corresponding minimum fuzzy factor when the PE index converges is selected to be the optimal fuzzy factor. The class number corresponding to the optimal fuzzy factor is the optimal class number, so that the class number of homogeneous regions in the remote sensing image is determined, and a better segmentation result can be obtained.

Description

[0001] Technical field: [0002] The invention relates to the field of image segmentation, in particular to a variable-type remote sensing image segmentation method based on optimal fuzzy factor selection. [0003] Background technique: [0004] Image segmentation is one of the important steps in image processing, the key issue is how to accurately determine the number of image categories and effectively segment homogeneous regions. Most of the existing image segmentation algorithms need to give the appropriate number of categories, among which FCM and its improved algorithms are widely used. Due to the wide coverage and complex types of ground objects in remote sensing images, it is difficult to artificially determine the number of image categories. Therefore, it is of great significance to achieve variable class segmentation of images. Currently, there are mainly statistical methods and clustering methods to achieve variable class image segmentation. Compared with statistic...

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): G06T7/10
CPCG06T2207/10032
Inventor 赵泉华刘晓燕李玉
Owner LIAONING TECHNICAL 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