CN104637027A - Time and space quantitative fusion method for remote sensing data considering nonlocal characteristics and temporal and spatial variation - Google Patents
Time and space quantitative fusion method for remote sensing data considering nonlocal characteristics and temporal and spatial variation Download PDFInfo
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Abstract
The invention discloses a time and space quantitative fusion method for remote sensing data considering nonlocal characteristics and temporal and spatial variation. The time and space quantitative fusion method is based on nonlocal filter, utilizes a moving window technique and comprises the following steps: firstly, selecting similar picture elements, screening the similar picture elements by using an experiential formula based on small window operation, and secondarily screening the similar picture elements on the basis; measuring the weight according to the similarity of neighborhood matrixes with the similar picture elements as the center as well as the relative distance between the neighborhood matrixes and the central picture element; considering the temporal and spatial variation, and aiming at main characteristics of the region, selecting corresponding modes for weighing; finally, fusing to obtain a reflectance value of the central picture element. According to the time and space quantitative fusion method disclosed by the invention, by using complementary information of high spatial resolution data and high temporal resolution data and considering the nonlocal characteristics and the temporal and spatial variation, data with high spatial resolution and high temporal resolution are obtained by fusing, so that the time and space quantitative fusion method has higher precision and greater actual application potential.
Description
Technical Field
The invention belongs to the field of remote sensing image fusion, and relates to a remote sensing data space-time quantitative fusion method considering non-local characteristics and space-time changes.
Background
The remote sensing monitoring with high resolution, long time sequence and high precision has great significance for the relevant fields of global change research, resource investigation and management, environmental monitoring and the like. However, due to the restriction of each index in the sensor design technology, the spatial resolution and the temporal resolution are mutually balanced. To solve this problem, a corresponding method is created that fuses and generates data having both high spatial resolution and high temporal resolution using complementary information of the satellite data of high spatial resolution and the satellite data of high temporal resolution, i.e., a space fusion technique. The current common fusion methods include a classical space-time adaptive reflectivity fusion method and a space-time adaptive reflectivity fusion method based on a spectrum unmixing theory. The two methods have respective advantages and limitations, the classical method has advantages in predicting regions mainly changing in time, and detail changes in the space of heterogeneous regions are difficult to predict; the enhanced algorithm is advantageous in predicting spatial detail changes, but less effective in predicting temporally changing regions.
Disclosure of Invention
The invention aims to provide a space-time quantitative fusion method based on non-local thought and considering time and space changes, and the space-time quantitative fusion is carried out by considering the time and space changes aiming at the characteristics of the prior art.
In order to capture regional characteristics better, the method aims at two image pair prediction, combines non-local filtering, uses an empirical formula similar pixel screening method based on small window operation, performs secondary screening on similar pixels, adopts first-order similar non-local mean weight, and predicts the reflectivity value of a window center pixel by weighted average of the similar pixels based on moving window operation.
The technical scheme adopted by the invention is as follows: a remote sensing data space-time quantitative fusion method considering non-local characteristics and space-time changes is characterized by comprising the following steps:
step 1: inputting a Tm moment high-space and low-space resolution image pair and a Tn moment high-space and low-space resolution image pair, and preprocessing the input images, wherein the preprocessing comprises reprojection, resampling or clipping;
step 2: the Tm time high spatial and low spatial resolution image pair and the Tn time high spatial and low spatial resolution image pair respectively use an empirical formula in combination with a time difference TijkAnd the spectral difference SijkScreening similar pixels;
and step 3: based on non-local filtering, calculating the weight of each similar pixel by adopting the similarity of a neighborhood matrix taking the similar pixel as a center and the relative distance between the similar pixel and the center pixel;
and 4, step 4: and (4) taking the time change and the spatial change into consideration, fusing the time-changed regions in a classical algorithm mode, and fusing the space-changed regions in a time weighting mode.
Preferably, the empirical formula described in step 2 is:
wherein, F (x)i,yjB) reflectance values of neighboring pels of the temporal high spatial resolution data on a basis, F (x)w/2,yw/2B) is the reflectivity of the central pixel, B is the number of wave bands, and d is a free parameter.
Preferably, the screening process is performed in a small window, i.e. the mean is calculated.
Preferably, the specific implementation process of step 3 is to calculate the weight of each similar pixel by a matrix-based method based on non-local filtering, perform spatial filtering in a first-order form, and add a relative distance on the basis, and the specific implementation manner is:
wherein,andrespectively, the mean values of the spectral difference and the time difference of the neighborhood matrix, and the experiment is carried out by adjusting the filtering parameter h and the matrix window, wherein the optimal h range is [0.001, 0.1 ]](ii) a D is adjacent pixel (x)i，yj) Relative distance from the center pixel element.
Preferably, the time-varying region in step 4 is fused by a classical algorithm, and the formula is as follows:
wherein F, C represents the reflectivity of the high spatial resolution and high temporal resolution data, respectively, (x)i,yj) For a given position of a high spatial resolution and high temporal resolution data pel, t0Time of data acquisition, tkFor the prediction time, w is the search window size, and n is the input image logarithm;
the space-time-varying regions in step 4 are fused in a time-weighted mode, and the formula is as follows:
F(xw/2,yw/2,tp)＝F(x,y,t0)+∑w(x,y)*aw/2*(C(x,y,tp)-C(x,y,t0) (4); it is characterized by that it calculates the linear regression coefficient a of similar picture element in the moving windoww/2And further calculating the time phase weight of each basic time, and weighting to obtain a final fusion result.
The method is characterized in that based on non-local filtering, similar pixel screening and weight calculation are carried out, space-time change is considered, corresponding modes are selected for weighting according to main characteristics of the region, and finally the reflectivity value of the central pixel is obtained through fusion. In a word, the method provided by the invention can be effectively used for the space-time quantitative fusion of the remote sensing images, and more accurate prediction results can be obtained.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Please refer to fig. 1, the method for spatiotemporal quantitative fusion of remote sensing data considering non-local characteristics and spatiotemporal changes provided in this embodiment is used for obtaining a reflectance value of a central pixel through fusion; the method comprises the following steps:
step 1: inputting a Tm moment high-space and low-space resolution image pair and a Tn moment high-space and low-space resolution image pair, and preprocessing the input images, wherein the preprocessing comprises reprojection, resampling or clipping;
step 2: the Tm time high spatial and low spatial resolution image pair and the Tn time high spatial and low spatial resolution image pair respectively use an empirical formula in combination with a time difference TijkAnd the spectral difference SijkScreening similar pixels;
the empirical formula is:
And step 3: based on non-local filtering, calculating the weight of each similar pixel by adopting the similarity of a neighborhood matrix taking the similar pixel as a center and the relative distance between the similar pixel and the center pixel;
based on Non-local filtering (Non-local means), the weight of each similar pixel is calculated by a matrix-based method, and the fact that the calculation of a second-order normal form may cause smooth transition is considered, so that excessive detail information is lost in a fusion result. Therefore, the invention adopts a first-order form to carry out spatial filtering, and adds a relative distance on the basis, and the specific implementation mode is as follows:
wherein,andrespectively, the mean values of the spectral difference and the time difference of the neighborhood matrix, and the experiment is carried out by adjusting the filtering parameter h and the matrix window, wherein the optimal h range is [0.001, 0.1 ]](ii) a D is adjacent pixel (x)i，yj) Relative distance from the center pixel element.
And 4, step 4: and (4) considering time change and space change, fusing the time-changed regions in a classical algorithm mode, fusing the time-changed regions in a time weighting mode, and finally fusing to obtain the reflectivity value of the central pixel.
The time-varying region (which is represented by the fact that the earth surface coverage (phenology) changes greatly in a certain time period) is fused in a classical algorithm mode, and the formula is as follows:
wherein F, C represents the reflectivity of the high spatial resolution and high temporal resolution data, respectively, (x)i,yj) For a given position of a high spatial resolution and high temporal resolution data pel, t0Time of data acquisition, tkFor the prediction time, w is the search window size, and n is the input image logarithm;
the fusion of the spatial variation area (the surface coverage (phenology) basically does not change with time or changes little, generally occurs in the spatial heterogeneous area) by adopting a time weighting mode, and the formula is as follows:
F(xw/2,yw/2,tp)＝F(x,y,t0)+∑w(x,y)*aw/2*(C(x,y,tp)-C(x,y,t0) (4); it is characterized by that it calculates the linear regression coefficient a of similar picture element in the moving windoww/2And further calculating the time phase weight of each basic time, and weighting to obtain a final fusion result.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A remote sensing data space-time quantitative fusion method considering non-local characteristics and space-time changes is characterized by comprising the following steps:
step 1: inputting a Tm moment high-space and low-space resolution image pair and a Tn moment high-space and low-space resolution image pair, and preprocessing the input images, wherein the preprocessing comprises reprojection, resampling or clipping;
step 2: the high-spatial and low-spatial resolution image pair at the Tm moment and the high-spatial and low-spatial resolution image pair at the Tn moment are respectively combined by using empirical formulasTime difference TijkAnd the spectral difference SijkScreening similar pixels;
and step 3: based on non-local filtering, calculating the weight of each similar pixel by adopting the similarity of a neighborhood matrix taking the similar pixel as a center and the relative distance between the similar pixel and the center pixel;
and 4, step 4: and (4) taking the time change and the spatial change into consideration, fusing the time-changed regions in a classical algorithm mode, and fusing the space-changed regions in a time weighting mode.
2. The method for spatiotemporal quantitative fusion of remote sensing data considering non-local characteristics and spatiotemporal changes according to claim 1, characterized in that the empirical formula in step 2 is:
wherein, F (x)i,yjB) reflectance values of neighboring pels of the temporal high spatial resolution data on a basis, F (x)w/2,yw/2B) is the reflectivity of the central pixel, B is the number of wave bands, and d is a free parameter.
3. The method for spatiotemporal quantitative fusion of remote sensing data considering non-local characteristics and spatiotemporal changes according to claim 2, characterized in that: the screening process is performed in a small window, i.e. the mean is calculated.
4. The method for spatiotemporal quantitative fusion of remote sensing data considering non-local characteristics and spatiotemporal changes according to claim 2, characterized in that: the specific implementation process of the step 3 is that based on non-local filtering, the weight of each similar pixel is calculated by a matrix-based method, spatial filtering is carried out by adopting a first-order form, and a relative distance is added on the basis, and the specific implementation mode is as follows:
wherein,andrespectively, the mean values of the spectral difference and the time difference of the neighborhood matrix, and the experiment is carried out by adjusting the filtering parameter h and the matrix window, wherein the optimal h range is [0.001, 0.1 ]](ii) a D is adjacent pixel (x)i，yj) Relative distance from the center pixel element.
5. The method for spatiotemporal quantitative fusion of remote sensing data considering non-local characteristics and spatiotemporal variations according to claim 2, 3 or 4, characterized in that:
and 4, fusing the time-varying regions in a classical algorithm mode, wherein the formula is as follows:
wherein F, C represents the reflectivity of the high spatial resolution and high temporal resolution data, respectively, (x)i,yj) For a given position of a high spatial resolution and high temporal resolution data pel, t0Time of data acquisition, tkFor the prediction time, w is the search window size, and n is the input image logarithm;
the space-time-varying regions in step 4 are fused in a time-weighted mode, and the formula is as follows:
F(xw/2,yw/2,tp)＝F(x,y,t0)+∑w(x,y)*aw/2*(C(x,y,tp)-C(x,y,t0)) (4)；
it is characterized by that it calculates the linear regression coefficient a of similar picture element in the moving windoww/2And then calculating each base timeAnd (5) weighting time phase weight to obtain a final fusion result.
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| CN110503137A (en) * | 2019-07-29 | 2019-11-26 | 电子科技大学 | Based on the determination method of the remote sensing image temporal-spatial fusion base image pair of mixing together |
| CN112419198A (en) * | 2020-11-27 | 2021-02-26 | 中国矿业大学 | Non-local mean weighting method for SAR interferogram filtering |
| CN112819697A (en) * | 2021-02-04 | 2021-05-18 | 北京师范大学 | Remote sensing image space-time fusion method and system |
| CN113702305A (en) * | 2021-08-17 | 2021-11-26 | 燕山大学 | Gas concentration linear measurement method based on self-adaptive differential absorption spectrum technology |
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Cited By (11)
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| CN112419198A (en) * | 2020-11-27 | 2021-02-26 | 中国矿业大学 | Non-local mean weighting method for SAR interferogram filtering |
| CN112419198B (en) * | 2020-11-27 | 2024-02-02 | 中国矿业大学 | Non-local mean weighting method for SAR interferogram filtering |
| CN112819697A (en) * | 2021-02-04 | 2021-05-18 | 北京师范大学 | Remote sensing image space-time fusion method and system |
| CN113702305A (en) * | 2021-08-17 | 2021-11-26 | 燕山大学 | Gas concentration linear measurement method based on self-adaptive differential absorption spectrum technology |
| CN113702305B (en) * | 2021-08-17 | 2022-07-15 | 燕山大学 | Gas concentration linear measurement method based on self-adaptive differential absorption spectrum technology |
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