Image enhancement technology and remote image classification method based on fuzzy set theory

A technology of image enhancement and fuzzy collection, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as not considering image fuzziness, weakening image details, etc.

Inactive Publication Date: 2018-07-10
GUANGDONG KINGPOINT DATA SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of these traditional image enhancement techniques do not consider the fuzziness of the image, but simply change

Method used

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  • Image enhancement technology and remote image classification method based on fuzzy set theory
  • Image enhancement technology and remote image classification method based on fuzzy set theory
  • Image enhancement technology and remote image classification method based on fuzzy set theory

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0114] As described above, the image enhancement technology and remote sensing image classification method based on fuzzy set theory, the difference of this embodiment is that the collection device also includes an image enhancement unit, and the image enhancement unit is used to adopt the method based on fuzzy set The Pal-King enhancement method for image enhancement; the image enhancement unit includes a fuzzy feature plane module, and the fuzzy feature plane module converts an M×N dimension with L gray levels according to the concept of fuzzy subset theory The image X of is treated as a fuzzy lattice, denoted as

[0115]

[0116] or

[0117]

[0118] in Indicates that the pixel at point (i, j) in the image has a certain characteristic degree is μ ij (0≤μ ij ≤1), called μ ij is a fuzzy feature. If the relative gray level of the pixel is taken as the fuzzy feature of interest, then μ ij Represents the grayscale x of the pixel (x,y) ij Regarding the membership of a...

Embodiment 2

[0130] As described above, the image enhancement technology and remote sensing image classification method based on fuzzy set theory, the difference in this embodiment is that the collection device also includes a remote sensing unit, and the remote sensing unit is used for the image enhancement unit After the image enhancement is completed, the remote sensing image is further classified and processed;

[0131] First, the fuzzy C-means clustering method is used to unsupervisedly classify the training samples. The fuzzy C-means clustering method obtains the degree of membership of each sample point to the class center by optimizing the fuzzy objective function J, thereby determining the attribution of the sample points . J is the sum of squared errors between each sample and its class mean:

[0132]

[0133] in

[0134]

[0135] m∈[1,∞) is the fuzzy weighting index,

[0136] X={x 1 ,x 2 ,...,x k ,...,x n} is the data set, x k ∈R p , R p is a p-dimensional space,...

Embodiment 3

[0154] As described above, the image enhancement technology and remote sensing image classification method based on fuzzy set theory, the difference of this embodiment is that the specific steps of the image enhancement technology and remote sensing image classification method based on fuzzy set theory are as follows:

[0155] S1: input image i=1,2,...,M; j=1,2,...,N;

[0156] S2: Construct the membership function

[0157] mu ij =F(X ij ) = log 2 [1+(X ij -T min ) / (T max -T min )]

[0158] where T min is the minimum value of the gray value, T max is the maximum value of the gray value;

[0159] S3: Forward transformation: perform blur enhancement transformation on the image, and repeatedly use nonlinear transformation as follows

[0160] μ′ ij =T r (μ ij ) = T 1 [T r-1 (μ ij )], r=1,2,3,4...

[0161] Among them, r is the number of iterations,

[0162]

[0163] S4: Repeat step S3 for X times, wherein, X≥0;

[0164] S5: Inverse transformation: after the ...

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Abstract

The invention provides an image enhancement technology and remote image classification method based on a fuzzy set theory. The image enhancement technology and remote image classification method basedon the fuzzy set theory are implemented through a set device. The set device comprises a fuzzy set unit and further comprises an image enhancement unit, wherein the image enhancement unit comprises afuzzy feature plane module, and the image enhancement unit further comprises a fuzzy enhancement transformation module. The set device also comprises a remote sensing unit. The image enhancement method based on the fuzzy set theory is applied to image processing, thereby overcoming a defect that the detail part of an image is weaken while the noise is suppressed because the traditional image enhancement technology does not take the fuzziness of the image into consideration but only simply changes the contrast or suppress the noise for the whole image.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an image enhancement technology and remote sensing image classification method based on fuzzy set theory. Background technique [0002] Images play a very important role in human perception, and the information conveyed by images is richer and more real than any other form. At present, digital image processing has become an important means for people to understand and transform the world. The highest purpose of digital image processing is to realize the classification or recognition of objects in digital images, that is, pattern recognition, so as to construct a machine system that automatically processes certain information to replace manual tasks of classification and recognition. This machine system is generally divided into four parts: information acquisition, preprocessing, feature extraction, and decision classification. Among them, the preprocessing part uses t...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T5/00
CPCG06T5/009G06T2207/10032G06V20/13G06F18/23213G06F18/24
Inventor 麻建吴剑文何伟潮单小红
Owner GUANGDONG KINGPOINT DATA SCI & TECH CO LTD
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