CN104463801A - Multi-sensing-information fusion method based on self-adaptation dictionary learning - Google Patents

Abstract

The invention discloses a multi-sensing-information fusion method based on self-adaptation dictionary learning. The multi-sensing-information fusion method comprises the steps that the maximum noise variance of an input image is estimated, the image is divided, dictionary learning is carried out, self-adaptation sparse coding is carried out, data fusion is carried out, image block reconstructing is carried out, and image reconstructing is carried out. The multi-sensing-information fusion method has the multi-scale and multi-direction advantages and the advantage that effective extracted features are represented in a sparse mode at the same time. The caused spectrum distortion is small, a high-quality fusion image is obtained, the scene of the fusion image is clear, the amount of information is large, and observing of the human eyes is better facilitated. A better fusion effect is obtained on the subjective vision effect and an objective evaluation index, and the fusion method is effective and feasible.

Description

A kind of multi-sensor data fusion method based on self-adapting dictionary study

Technical field

The present invention relates to a kind of information fusion method, particularly a kind of multi-sensor data fusion method.

Background technology

The object of multi-sensor data fusion be exactly utilize different sensors data between complementarity and redundancy, the information of different sensors is merged, make the information after fusion while keeping original spectrum characteristic, increase the spatial detail information of image as much as possible, thus the maximum information obtaining target scene describes, this not only increases the quality of remote sensing images, and be more conducive to the subsequent treatment such as classification and target identification of remote sensing images.

Summary of the invention

In order to overcome defect of the prior art, solving the problems of the technologies described above, the invention provides a kind of multi-sensor data fusion method based on self-adapting dictionary study.

Based on a multi-sensor data fusion method for self-adapting dictionary study, step is:

Estimate the maximum noise variance of input picture;

Image block;

Dictionary learning;

Adaptive sparse is encoded;

Data fusion;

Image block reconstructs;

Image reconstruction.

Described image block comprises:

By all images to be fused respectively according to atom size by blocks of pixels;

Block is lined up sample matrix by column vector mode;

Sample set is formed by sample matrix.

Described dictionary learning comprises:

The new training sample of several compositions of sample is got at random from sample set;

Average is gone to new training sample, then obtains self-adapting dictionary through interative computation.

Described sparse coding comprises:

Sample matrix is adopted on self-adapting dictionary ASP algorithm realization Its Sparse Decomposition, obtain sparse coefficient matrix.

Described data fusion comprises:

Certain fusion rule is adopted by sparse coefficient matrix to select characteristic remarkable property coefficient as fusion coefficients.

Described image block reconstruct comprises:

Sparse for the fusion convolution with crossing complete dictionary is realized block reconstruct, and obtain reconstructed image block vector.

Described Image Reconstruction comprises:

By reconstructed image block vector, revert to image block data, add average and rearrange according to order during piecemeal;

Overlapping block is averaged and realizes image reconstruction, obtain last fused images.

Beneficial effect of the present invention:

The present invention has the characteristic that multiple dimensioned, multidirectional feature and rarefaction representation effectively extract feature simultaneously, and the spectrum distortion caused is less, obtains high-quality fused images, and fused images scene is clear, contain much information, and is more conducive to the observation of human eye.Subjective vision effect and objective evaluation index all achieve more excellent syncretizing effect, are a kind of effective and feasible fusion methods.

Accompanying drawing explanation

Fig. 1 is the image co-registration process schematic represented based on adaptive sparse.

Embodiment

Hereafter will describe embodiments of the invention in detail by reference to the accompanying drawings.It should be noted that the combination of technical characteristic or the technical characteristic described in following embodiment should not be considered to isolated, they can mutually be combined thus be reached better technique effect.In the accompanying drawing of following embodiment, the identical label that accompanying drawing occurs represents identical feature or parts, can be applicable in different embodiment.

As shown in Figure 1, the interative computation process crossing complete rarefaction representation can obtain the complete dictionary of mistake and the sparse coefficient of training sample simultaneously.For image co-registration task, the selection of sparse coefficient determines last syncretizing effect.With the two width remote sensing images that registration is good, its concrete fusion process is as follows:

The maximum noise variances sigma of step one, estimation input picture ², make T=λ σ ², wherein T is sparse threshold value, and λ is coefficient.

If step 2, image block atom size are set as M, M=n × n, so, need block image a, b to be fused being divided into respectively P1 and P2 n × n size according to atom size by pixel, if two image sizes to be fused are identical, then P1=P2=P, block is lined up sample matrix Y1 and Y2 by column vector mode, and form sample set F by Y1 and Y2, F=[Y1, Y2].

Step 3, dictionary learning get the new training sample Y of P composition of sample at random from F, and the size of Y is M × N, first goes average to Y, and the step then according to Section 1 obtains self-adapting dictionary D through interative computation.

Y1 and Y2 is adopted ASP algorithm realization Its Sparse Decomposition by step 4, sparse coding on dictionary D, obtains sparse coefficient matrix α ₁and α ₂, the corresponding image block of its each row.

Step 5, data fusion are by α ₁and α ₂certain fusion rule is adopted to select characteristic remarkable property coefficient as fusion coefficients α.Here fusion rule choose comparatively crucial, this fusion rule is chosen based on the self-adaptation of the weighting coefficient of region energy.First the local energy E of position centered by point (m, n) is obtained _jA(m, n) and E _jB(m, n), after can to calculate the rarefaction representation coefficient of fused images according to following formula:

$\mathrm{\α}=\frac{E_{\mathrm{JA}}(m,n)}{E_{\mathrm{JA}}(m,n)+E_{\mathrm{JB}}(m,n)}{\∂}_1+\frac{E_{\mathrm{JB}}(m,n)}{E_{\mathrm{JA}}(m,n)+E_{\mathrm{JB}}(m,n)}{\mathrm{\α}}_2$

Because center pixel that energy of local area is larger represents the obvious characteristic of original image, if a certain region energy of being tried to achieve image A by above-mentioned formula is larger, then corresponding weighting coefficient also can be larger, if region energy is less, then corresponding weighting coefficient also can be less, meets the feature that the feature of original image own affects weighting coefficient.So this adaptive fusion rule, be effective, feasible.

Step 6, image block reconstruct fusion coefficients realizes block with the convolution crossing complete dictionary and reconstructs, namely for the reconstructed image block after merging is vectorial.

Step 7, image reconstruction are by y _irevert to image block data, add average and rearrange according to order during piecemeal, overlapping block is averaged and realizes image reconstruction, obtain last fused images Y.

The present invention has the characteristic that multiple dimensioned, multidirectional feature and rarefaction representation effectively extract feature simultaneously, and the spectrum distortion caused is less, obtains high-quality fused images, and fused images scene is clear, contain much information, and is more conducive to the observation of human eye.Subjective vision effect and objective evaluation index all achieve more excellent syncretizing effect, are a kind of effective and feasible fusion methods.

Although give some embodiments of the present invention, it will be understood by those of skill in the art that without departing from the spirit of the invention herein, can change embodiment herein.Above-described embodiment is exemplary, should using embodiment herein as the restriction of interest field of the present invention.

Claims (7)

1., based on a multi-sensor data fusion method for self-adapting dictionary study, it is characterized in that, step is:

Estimate the maximum noise variance of input picture;

Image block;

Dictionary learning;

Adaptive sparse is encoded;

Data fusion;

Image block reconstructs;

Image reconstruction.

2. as claimed in claim 1 a kind of based on self-adapting dictionary study multi-sensor data fusion method, it is characterized in that, described image block comprises:

By all images to be fused respectively according to atom size by blocks of pixels;

Block is lined up sample matrix by column vector mode;

Sample set is formed by sample matrix.

3. as claimed in claim 1 a kind of based on self-adapting dictionary study multi-sensor data fusion method, it is characterized in that, described dictionary learning comprises:

The new training sample of several compositions of sample is got at random from sample set;

Average is gone to new training sample, then obtains self-adapting dictionary through interative computation.

4. as claimed in claim 1 a kind of based on self-adapting dictionary study multi-sensor data fusion method, it is characterized in that, described sparse coding comprises:

Sample matrix is adopted on self-adapting dictionary ASP algorithm realization Its Sparse Decomposition, obtain sparse coefficient matrix.

5. as claimed in claim 1 a kind of based on self-adapting dictionary study multi-sensor data fusion method, it is characterized in that, described data fusion comprises:

Certain fusion rule is adopted by sparse coefficient matrix to select characteristic remarkable property coefficient as fusion coefficients.

6. a kind of multi-sensor data fusion method based on self-adapting dictionary study as claimed in claim 1, it is characterized in that, the reconstruct of described image block comprises:

Sparse for the fusion convolution with crossing complete dictionary is realized block reconstruct, and obtain reconstructed image block vector.

7. as claimed in claim 1 a kind of based on self-adapting dictionary study multi-sensor data fusion method, it is characterized in that, described Image Reconstruction comprises:

By reconstructed image block vector, revert to image block data, add average and rearrange according to order during piecemeal;

Overlapping block is averaged and realizes image reconstruction, obtain last fused images.