CN110046674A - Classification of hyperspectral remote sensing image method based on feedback arrangement - Google Patents
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Abstract
Classification of hyperspectral remote sensing image method based on feedback arrangement, belongs to electronic information technical field, and the present invention is solves the problems, such as existing hyperspectral image classification method there are niceties of grading that the low, training time is grown.The present invention is classified in hyperspectral image data using support vector machines；Judge whether classification results meet stop condition, if it is terminate iteration, classification results at this time are final classification as a result, if otherwise generating bianry image block；Space filtering is carried out to bianry image block；It is fed back, is stacked with the hyperspectral image data of input；Then the number of iterations is added one, return re-starts classification, until terminating iteration using classification results as final classification result.The present invention is used for remote sensing technology.
Description
Technical field
The present invention relates to a kind of classification methods of high-spectrum remote sensing, belong to electronic information technical field.
Background technique
With the development of remote sensing technology and imaging spectral technology, high-spectrum remote sensing becomes the mankind and observes earth surface
One of important tool.High light spectrum image-forming technology combines imaging technique and spectrographic detection technology, for each space pixel several
Hundred so thousands of continuous narrow-bands on be imaged.The maximum difference of itself and traditional remote sensing images is embodied in two o'clock: first is that
The coverage area of spectral coverage is wider, high spectrum image may include ultraviolet, visible light, near-infrared and in infrared multiple spectral coverage ranges,
And traditional remote sensing images only include visible-range；Second is that wave band is narrower, the waveband width of general high spectrum image is on the left side 10nm
The right side, and traditional remote sensing images generally only include three wave bands of red, green, blue, and width is generally several hundred nanometers.Therefore, EO-1 hyperion
Image had both included spatial surface information, also included spectral information.
Due to different atural objects on different-waveband different, the more traditional remote sensing images of high spectrum image to the absorption of spectrum
More terrestrial object informations are capable of providing, there is better atural object identification capability.High spectrum image terrain classification is high spectrum image
One of important application, be divided into Supervised classification and unsupervised segmentation two major classes, wherein Supervised classification is the presence of training number
According to the classification under the conditions of i.e. prior information, parameter training first generally is carried out to classifier with training data, then with trained
Classifier classifies to the data newly obtained, and unsupervised data are the blind classification without using prior information.Due to being imaged
The complexity of journey and the diversity of atural object mainly use supervised classification method in practical application.
In recent years, researcher proposes the hyperspectral image classification method for much having supervision, including classical supporting vector
Machine method and the convolutional neural networks method developed rapidly in recent years.It is asked however, these methods still have following two
Topic:
1) in the case where training sample is less, nicety of grading is lower；
2) generally longer in convolutional neural networks method as the training time of the classification method of representative, it cannot be effectively in reality
It is used in the application of border.
Summary of the invention
That there are niceties of grading the invention aims to solving existing hyperspectral image classification method is low, the training time is long
Problem provides a kind of Classification of hyperspectral remote sensing image method based on feedback arrangement.
Classification of hyperspectral remote sensing image method of the present invention based on feedback arrangement, detailed process are as follows:
S1, hyperspectral image data conduct input number of the format for a × b × N, comprising M atural object classification will be formed substantially
According to Ω (k), and enable the number of iterations k=0；
S2, in k iteration, classified on hyperspectral image data Ω (k) using support vector machines, classification results
ForWherein SVM indicates support vector machines, C presentation class, and MAP indicates Maximun Posterior Probability Estimation Method,It indicates to carry out classifying using support vector machines for a variable and utilizes Maximun Posterior Probability Estimation Method generation
Classification results；
S3, as k > 0, judgementWhether meet stop condition, if it is terminates iteration, at this timeFor final classification as a result, if otherwise executing S4；
S4, basisGenerate bianry image block
In j-th of image, position is that the value of the pixel of (x, y) and the pixel existOn classification it is corresponding, then
It is 1 that position, which is the value of the pixel of (x, y), in j-th of image, is otherwise 0；
S5, the bianry image block that S4 is generatedSpace filtering is carried out, filtered gray level image block is
S6, the filtered gray level image block for obtaining S5It is fed back, the high spectrum image with input
Data Ω (k) is stacked:
Then k=k+1 is enabled, and returns and executes S2, until terminating iteration willAs final classification result.
Preferably, using Tanimoto coefficient value TI as the condition for differentiating stopping:
And
Wherein,Indicate setIn element number,
Indicate setIn element number,
Indicate the classification results for carrying out the jth class of classification acquisition in kth time iteration by support vector machines,
Indicate the classification results for carrying out the jth class of classification acquisition in -1 iteration of kth by classifier,
TI(k)Indicate the Tanimoto coefficient value in kth time iteration,
Indicate the Tanimoto coefficient value of jth width classification results image in kth time iteration.
Advantages of the present invention: the Classification of hyperspectral remote sensing image method proposed by the present invention based on feedback arrangement, it can be most
Posterior information is converted prior information by the extraction spatial information of limits, and each iteration all increases a part of spatial information, real
Making full use of for spatial information is showed.This method can be obviously improved classification compared with existing hyperspectral image classification method
Precision, to existing public data collection such as Purdue ' s Indian Pine, in the condition that 10% sample point of selection is trained
Under, nicety of grading reaches 97% or more.In addition, existing nicety of grading is preferably based on convolutional Neural under identical conditions
The method of network generally requires the training time of a few hours, and the training time of this method greatly shortens, and about 5-10 minutes.
Detailed description of the invention
Fig. 1 is the schematic diagram of the Classification of hyperspectral remote sensing image method of the present invention based on feedback arrangement.
Specific embodiment
Specific embodiment 1: illustrating present embodiment below with reference to Fig. 1, based on feedback arrangement described in present embodiment
Classification of hyperspectral remote sensing image method, detailed process are as follows:
S1, hyperspectral image data conduct input number of the format for a × b × N, comprising M atural object classification will be formed substantially
According to Ω (k), and enable the number of iterations k=0；
S2, in k iteration, classified on hyperspectral image data Ω (k) using support vector machines, classification results
ForWherein SVM indicates support vector machines, C presentation class, and MAP indicates Maximun Posterior Probability Estimation Method,Point that carries out classifying using support vector machines and generate using Maximun Posterior Probability Estimation Method is indicated for a variable
Class result；
S3, as k > 0, judgementWhether meet stop condition, if it is terminates iteration, at this timeFor final classification as a result, if otherwise executing S4；
S4, basisGenerate bianry image block
In j-th of image, position is that the value of the pixel of (x, y) and the pixel existOn classification it is corresponding, then
It is 1 that position, which is the value of the pixel of (x, y), in j-th of image, is otherwise 0；
S5, the bianry image block that S4 is generatedSpace filtering is carried out, filtered gray level image block is
S6, the filtered gray level image block for obtaining S5It is fed back, the high spectrum image with input
Data Ω (k) is stacked:
Then k=k+1 is enabled, and returns and executes S2, until terminating iteration willAs final classification result.
Stop condition described in S3 are as follows:
Using Tanimoto coefficient value TI as the condition for differentiating stopping:
And
Wherein,Indicate setIn element number,
Indicate setIn element number,
Indicate the classification results for carrying out the jth class of classification acquisition in kth time iteration by support vector machines,
Indicate the classification results for carrying out the jth class of classification acquisition in -1 iteration of kth by classifier,
TI(k)Indicate the Tanimoto coefficient value in kth time iteration,
Indicate the Tanimoto coefficient value of jth width classification results image in kth time iteration.
In present embodiment, each classification corresponds to a classification results image, therefore, the j in classification results and j-th
J in image is a variable.
In the present invention, in the selection of spatial filter, there are several types of:
1, Gaussian filter:
It is the most common spatial filter based on Gaussian Profile:
2, Steerable filter device:
Comprising a reference image R and input picture I to be filtered, enabling output image is O, then:
O(xi,yi)=∑ W(x,y)(R(xi,yi))I(xi,yi)；
Wherein, (xi,yi) and (x, y) be respectively the coordinate of pixel in correspondence image, W(x,y)(R(xi,yi)) it is by with reference to figure
It is mutually indepedent with I as the kernel function that R is obtained.
3, Gabor filter:
It is a kind of for simulating the filter of human visual system, is generally used for the extraction and identification of texture information.Two
On dimension space domain, which is made of the Gaussian function modulated by sine curve:
G (x, y)=exp (- π [(x-x0)2α2+(y-y0)2β2])·exp(-πi[μ0(x-x0)2+υ0(y-y0)2]) wherein,
(x0,y0)、(μ0,υ0) and (α, β) be respectively location parameter, modulation parameter and scale parameter.
Claims (2)
1. the Classification of hyperspectral remote sensing image method based on feedback arrangement, which is characterized in that its detailed process are as follows:
S1, it will form that format is a × b × N, the hyperspectral image data comprising M atural object classification is as input data Ω substantially
(k), and the number of iterations k=0 is enabled；
S2, in k iteration, classified on hyperspectral image data Ω (k) using support vector machines, classification results areWherein SVM indicates support vector machines, C presentation class, and MAP indicates Maximun Posterior Probability Estimation Method,Point that carries out classifying using support vector machines and generate using Maximun Posterior Probability Estimation Method is indicated for a variable
Class result；
S3, as k > 0, judgementWhether meet stop condition, if it is terminates iteration, at this timeFor final classification as a result, if otherwise executing S4；
S4, basisGenerate bianry image block
In j-th of image, position is that the value of the pixel of (x, y) and the pixel existOn classification it is corresponding, then jth
It is 1 that position, which is the value of the pixel of (x, y), in a image, is otherwise 0；
S5, the bianry image block that S4 is generatedSpace filtering is carried out, filtered gray level image block is
S6, the filtered gray level image block for obtaining S5It is fed back, the hyperspectral image data with input
Ω (k) is stacked:
Then k=k+1 is enabled, and returns and executes S2, until terminating iteration willAs final classification result.
2. the Classification of hyperspectral remote sensing image method according to claim 1 based on feedback arrangement, which is characterized in that S3 institute
State stop condition are as follows:
Using Tanimoto coefficient value TI as the condition for differentiating stopping:
And
Wherein,Indicate setIn element number,
Indicate setIn element number,
Indicate the classification results for carrying out the jth class of classification acquisition in kth time iteration by support vector machines,
Indicate the classification results for carrying out the jth class of classification acquisition in -1 iteration of kth by classifier,
TI(k)Indicate the Tanimoto coefficient value in kth time iteration,
Indicate the Tanimoto coefficient value of jth width classification results image in kth time iteration.
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Application publication date: 20190723 |