CN1489111A - Remote-sensing image mixing method based on local statistical property and colour space transformation - Google Patents

Abstract

The invented method processes remote sensing image through following steps. Based on IHS transform of multiple spectrum images, using statistic characteristics of remote sensing image, lowpass filtering is carried out for I-component of multiple spectrum images. Meanwhile, highpass filtering is carried out for panchromatic remote sensing image with high spatial resolution. Through histogram matching in local window, gray scale mapping is carried out one pixel by one pixel for value of pixel in panchromatic image with high resolution according to position of pixel. Thus, syncretic I-component is obtained. Then, IHS inverse transform is carried out so as to obtain syncretic result. The invention possesses advantages of retaining spectrum information of multiple spectrum images, raising spatial resolution, accurate decrypting features of ground object so that the invention is more suitable to each application and easy of interpretation.

Description

Remote sensing image fusing method based on partial statistics characteristic and color space transformation

Technical field:

The present invention relates to a kind of remote sensing image fusing method (LSC method) based on partial statistics characteristic and color space transformation (IHS conversion), is a Multi-Sensory Image Fusion at Pixel Level in the art of image analysis, has a wide range of applications in fields such as agricultural, military affairs.

Background technology:

Development along with remote sensing technology, modern remote sensing system can provide the view data of multiple high spatial resolution, spectral resolution and temporal resolution for the user, and the application of remote sensing technology progressively turns to the analysis-by-synthesis and the application of multiband, multisensor, multi-platform, multidate, many resolving powers data from the analytical applications of single-sensor data.

Obtaining at present of high-resolution satellite image can be carried out with two kinds of different modes: a kind of is the panchromatic mode of high spatial resolution, and another kind is the multispectral mode of high spectral resolution.The feature of panchromatic image is to have very high spatial resolution, is particularly suitable for medium scale drawing application and city and analyzes; Multispectral image provides atural object abundant spectral information, is suitable for small scale thematic charting (charting with figure as the soil).For the enrichment of advantage separately of the basic spectral information of the high spatial resolution of panchromatic image and low resolution multispectral image is got up, this two classes image can be merged, the image that obtains should have higher geological information content, has still kept good spectral information quality simultaneously.The fusion method of being taked should not make the spectral characteristic of original multispectral image distort, and guarantees that spectrum can divide in raw data those targets remain can divide in merging image.Such fusion results not only allows to the more accurate description of characters of ground object they to be more suitable in various application, and according to they original spectrum signs, easier decipher.Studies have shown that the reservation of spectral information is specially adapted to vegetational analysis and urban mapping

The image and the multispectral image (low resolution) of high spatial resolution are merged, and method traditional in this technology has: IHS converter technique, PCA (principal component analysis (PCA)) conversion, HPF (high-pass filtering) method etc.Aspect the spectral information protection, HPF method syncretizing effect is all better than IHS converter technique, PCA conversion.Its basic thought is at first multispectral image to be carried out the IHS conversion, then with the geological information in the high resolution image by pixel in the low resolution image that is added to and merge.The high-pass filtering component of high resolution image, corresponding to the high fdrequency component of image space, on the low resolution that the is added to image, the high geological information content of high resolution image is apparent in fusion results.High-pass filtering method is in fusion process, and directly the I component with multispectral image merges, and makes spectral information that variation take place.Merge the effect that to improve visual interpretation of image, improve the precision of classification drawing, but it can not be used for atural object identification and inverting work.

Summary of the invention:

The objective of the invention is to deficiency, a kind of remote sensing image fusing method based on partial statistics characteristic and IHS conversion is provided, when improving the multispectral image spatial resolution, keep the spectral information of multispectral image better at the prior art existence.

For realizing such purpose, the innovative point of technical solution of the present invention is: multispectral image is carried out on the basis of IHS conversion, utilize the statistical property of remote sensing image, multispectral image I (intensity) component is carried out low-pass filtering, get rid of the space structure information in the multispectral image, keep the spectral information in the multispectral image, the panchromatic remote sensing image to high spatial resolution carries out high-pass filtering simultaneously.Carry out local window histogram coupling by the remote sensing image to the filtering of two width of cloth process, the position that the pixel value of high resolving power panchromatic image is according to pixels put, carry out grey scale mapping by pixel, the I component after obtaining merging carries out the IHS inverse transformation again, obtains fusion results.Merge the spectral information that image keeps former low resolution multispectral image as far as possible in order to make, the local window image should keep the average and the variance of the corresponding local window of former low resolution multispectral image in the mapping process.

Remote sensing image fusing method of the present invention comprises following concrete steps:

1. multispectral image is carried out the IHS color space transformation, obtain the I component of multispectral image. exists

In the IHS space, keep H, the S component is constant;

2. with gauss low frequency filter the multispectral image I component is carried out filtering, get rid of in the multispectral image

Space structure information, keep the spectral information in the multispectral image.In filtering, to using

Each pixel in the wicket of filtering is got different weights.Weights are according to each picture of wicket

The size of the distance of vegetarian refreshments and center pixel is come out with gaussian curve approximation, and is carried out

Normalized, normalized are in order to guarantee that average and variance in the wicket of filtering front and back do not have

Too big variation is arranged; Pixel during away from the wicket central pixel point with little weights, otherwise with big

Weights;

3. with Hi-pass filter high resolution image is carried out filtering, Hi-pass filter window size and step 2

The middle used window size of low-pass filter is consistent;

4. the histogram of two width of cloth through the remote sensing image of filtering mated, finish the fusion of image.Coupling

Process be exactly that the pixel value of high resolving power panchromatic image is carried out grey scale mapping, obtain merging the back shadow

The I component of picture.In mapping process,, make and merge the part, back according to the statistical property of remote sensing image

The I component of window image keeps the corresponding local window of the I component of former low resolution multispectral image

Average and variance;

5. H, the S component process IHS colored inversion of the intensity image I ' after merging with former multispectral shadow

Change the multispectral image after obtaining to merge.

Image interfusion method of the present invention adopts low-pass filter that the low resolution image is carried out filtering, the analog value of the local mean value of panchromatic image and variance yields and original low-resolution spectra image mates, taken the original spectrum information of the spatial information and the low resolution image of high resolving power panchromatic image simultaneously into account, merged image high resolution structures information and come from the high resolving power panchromatic image.The image that merges is in the spectral information that keeps multispectral image well, improved the spatial resolution of multiresolution remote sensing image, fusion results is more suitable in various application them not only to the more accurate description of characters of ground object, and according to they original spectrum signs, easier decipher.

Embodiment:

In order to understand technical scheme of the present invention better, below embodiments of the present invention are further described.The concrete implementation detail of each several part is as follows:

1. multispectral image is carried out the IHS conversion:

With IHS transform method commonly used multispectral image is handled, multispectral image is transformed to the IHS space, obtain three components of I, H, S of multispectral image; In the IHS space, keep H, the S component is constant, I component is handled.

2. with gauss low frequency filter the multispectral image I component is carried out filtering

1) adopting size is the window of w * h, according to formula (1) multispectral image is carried out filtering (in the actual computation, window size can be taken as 5 * 5,9 * 9,15 * 15 etc.).

$\stackrel{\‾}{L}(i,j;w,h)=\underset{i=1}{\overset{w}{\mathrm{\Σ}}}\underset{j=1}{\overset{h}{\mathrm{\Σ}}}\mathrm{\ω}(w,h)L(i,j;w,h)......\left(1\right)$

Wherein (i j) is the weights that filtering is adopted to ω; L (i, j; W h) is the pixel value of multispectral image I component before the filtering; L (i, j; W h) is the pixel value of multispectral image I component after the filtering.

2) calculate weights

Calculate according to following method

(a) by formula: $F\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}}{{\&Integral;}_{-\&infin;}^{x}e}^{-\frac{t^2}{2}}\mathrm{dt}-\&infin;<x<+\&infin;$ Calculate.According to the size of the distance of each pixel of wicket and center pixel, go out the weights of each pixel of wicket with the formula fitting that provides.Each pixel in the window is got different weights, pixel during away from central pixel point with little weights, otherwise with big weights.

(b) average and the not too big variation of variance in order to guarantee wicket carried out normalized to weights, promptly satisfies condition:

$\underset{i=1}{\overset{w}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{h}{\mathrm{\&Sigma;}}}\mathrm{\&omega;}(w,h)=1......\left(2\right)$

3. with Hi-pass filter the high resolving power panchromatic image is carried out filtering:

Specific practice is: with moving window (window size can be taken as 5 * 5,9 * 9,15 * 15 etc.), according to following formula, entire image is carried out filtering:

$\stackrel{\&OverBar;}{H}(i,j)=\frac{1}{w\&times;h}\underset{i=1}{\overset{w}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{h}{\mathrm{\&Sigma;}}}H(i,j)......\left(3\right)$

Wherein: H (i, j) be the pixel value of filtered high resolving power panchromatic image, (i j) is the pixel value of high resolving power panchromatic image before the filtering to H, the window that filtering is used is w * h (can be taken as 5 * 5,9 * 9,15 * 15, be consistent with window size in the step 2)

4. the histogram of two width of cloth through the remote sensing image of filtering mated, finish the fusion of image.

By local window histogram coupling, finish fusion.Promptly (i, j) (i, pixel value j) carries out grey scale mapping to locational high resolving power panchromatic image H, the F after obtaining merging (i, pixel value j).Calculate and adopt following formula:

$F(i,j)=\frac{S{\left(F\right)}_{(i,j;w,h)}\&lsqb;H(i,j)-\stackrel{\&OverBar;}{H}(i,j;w,h)\&rsqb;}{S{\left(H\right)}_{i,j;w,h}}+\stackrel{\&OverBar;}{F}(i,j;w,h).....\left(4\right)$

S (F) wherein _{(i, j; W, h)}, S (L) _{(i, j; W, h)}Be respectively that (i is that the local standard of image and low resolution image is poor after the fusion of center w * h window size j), F (i, j with pixel; W h) is the local mean value that merges the back image.

Merge the spectral information that image keeps former low resolution multispectral image as far as possible in order to make, that is to say, the low-frequency component that merges in the image is identical or very approaching with the low-frequency component of former low resolution multispectral image, merge average and variance that back local window image should keep the corresponding local window of former low resolution multispectral image, promptly satisfy formula (5), (6)

S(F) _{(i，j；w，h)}＝S(L) _{(i，j；w，h)}?????(5)

F (i, j; W, h)=L (i, j; W, h) (6) obtain following computing formula with in formula (5), (6) substitution formula (4):

$F(i,j)=\frac{S{\left(L\right)}_{i,j;w,h}\&lsqb;H(i,j)-\stackrel{\&OverBar;}{H}(i,j;w,h)\&rsqb;}{S{\left(H\right)}_{i,j;w,h}}+\stackrel{\&OverBar;}{L}(i,j;w,h)......\left(7\right)$

5. H, the S component process IHS colored inverse transformation of the intensity image I ' after merging with former multispectral shadow, the multispectral image after can obtaining to merge.

Table 1 has provided the comparison of the bias exponent of HPF method and LSC method fusion results.Window size is 15*15.Image and the matching degree of raw video on spectral information after bias exponent is used for reflecting and merges.If bias exponent is less, the image spectral information after then explanation is merged is preserved relatively goodly.

As can be seen from the table, the fusion results that the LSC method obtains, the bias exponent of each wave band is more much smaller than the fusion results that obtains with the HPF method.

The comparison of the bias exponent of table 1:HPF method and LSC method fusion results

	????HPF	????LSC
			The R component	????0.55812	????0.23094
The G component	????0.58764	????0.24377
			The B component	????0.6572	????0.28124

Claims (1)

1, a kind of remote sensing image fusing method based on partial statistics characteristic and color space transformation is characterized in that comprising following concrete steps:

1) multispectral image is carried out the IHS color space transformation, obtain the I component of multispectral image, in the IHS space, keep H, the S component is constant;

2) with gauss low frequency filter the multispectral image I component is carried out filtering, remove the space structure information in the multispectral image, keep the spectral information in the multispectral image, in filtering, each pixel to the wicket that is used for filtering is got different weights, weights come out with gaussian curve approximation according to the size of the distance of each pixel of wicket and center pixel, and carry out normalized; Pixel during away from the wicket central pixel point with little weights, otherwise with big weights;

3) with Hi-pass filter high resolution image is carried out filtering;

4) histogram of two width of cloth through the remote sensing image of filtering mated, finish the fusion of image, the process of coupling is exactly that the pixel value of high resolving power panchromatic image is carried out grey scale mapping, obtain merging the I component of back image, in mapping process, according to the statistical property of remote sensing image, make the I component that merges back local window image keep the average and the variance of the corresponding local window of I component of former low resolution multispectral image;

5) I component that merges the back image and original H, S component are carried out the IHS inverse transformation, obtain fusion results.