CN113870110A - Image fusion method and device for remote sensing image, electronic equipment and storage medium - Google Patents
Image fusion method and device for remote sensing image, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides an image fusion method of remote sensing images, which comprises the following steps: and respectively obtaining a plurality of high spatial resolution multispectral remote sensing images and n low spatial resolution multispectral remote sensing images. And synthesizing the n low-spatial-resolution multispectral remote sensing images to obtain a first panchromatic band image. And synthesizing the high-spatial-resolution multispectral remote sensing images to obtain a second panchromatic band image. And performing GS transformation on the original n low-spatial-resolution multispectral remote sensing images based on a phase recovery method. And carrying out GS inverse transformation on the plurality of GS transformed waveband images to obtain a fused image. An image fusion device, an electronic device and a storage medium of the remote sensing image are also provided. The image fusion method, the image fusion device, the electronic equipment and the storage medium for the remote sensing image can improve the spatial resolution of the multispectral remote sensing image with low spatial resolution and keep the spectral characteristics unchanged.
Description
Technical Field
The present invention relates to the field of image processing technologies for remote sensing images, and in particular, to an image fusion method and apparatus for remote sensing images, an electronic device, and a storage medium.
Background
The sentinel 2A/B satellite is a multispectral imaging satellite emitted by the European space agency in 2015 at 6 months and is mainly used in the fields of environment monitoring, quantitative parameter inversion, surface change monitoring and the like. The breadth of the satellite image acquisition system reaches 290 kilometers, the shortest earth observation revisit period of a two-satellite networking can reach three days, and the application range of the satellite image is greatly improved. Meanwhile, the image quality is greatly improved by the carried high-performance imaging load. The parameters of the sentinel 2A/B band are as follows:
sentinel 2A/B image parameter table
As can be seen from the above table, the satellite has 4 high spatial resolution bands of 10 meters and6 low spatial resolution bands of 20 meters. How to increase the spatial resolution of 6 20-meter wave bands to 10 meters has very important practical value.
In the prior art, most of remote sensing image fusion (panschargeing) methods are fusion of a panchromatic high-resolution image and a plurality of low-resolution multispectral images, and research on how the plurality of high-resolution multispectral images are fused with the low-resolution multispectral images is very little. For a sentinel 2A/B satellite with a plurality of high spatial resolution wave bands and a plurality of low spatial resolution wave bands, the fusion can not be carried out by the prior art, so that a new image fusion method of remote sensing images is required to be provided.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an image fusion method, an image fusion device, electronic equipment and a storage medium for remote sensing images.
In order to achieve the purpose, the invention adopts the technical scheme that:
an image fusion method of remote sensing images comprises the following steps:
respectively obtaining a plurality of high spatial resolution multispectral remote sensing images and n low spatial resolution multispectral remote sensing images; wherein n is more than or equal to 1;
carrying out geometric registration and resampling on the n low spatial resolution multispectral remote sensing images, and synthesizing to obtain a simulated first panchromatic band image;
synthesizing the high-spatial-resolution multispectral remote sensing images to obtain a simulated second panchromatic band image;
based on a phase recovery method, taking the first panchromatic waveband image as a first component of GS conversion, performing GS conversion on the original n low-spatial-resolution multispectral remote sensing images, and outputting a plurality of GS-converted waveband images, wherein the image corresponding to the first panchromatic waveband image is the GS-converted first waveband image1And the images respectively corresponding to the rest of the low spatial resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1；
Modifying the second panchromatic waveband image according to the first component of the GS conversion to obtain a modified image;
replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband videos as a first component of the GS inverse transformation, outputting n +1 GS inversely transformed waveband videos, and removing the GS inversely transformed first waveband video corresponding to the first component of the GS inverse transformation to obtain a fused video.
The further improvement of the technical scheme is as follows:
the image fusion method of the remote sensing images is characterized in that a sentinel 2A/B satellite is used for obtaining a plurality of high-spatial-resolution multispectral remote sensing images and a plurality of low-spatial-resolution multispectral remote sensing images, wherein the number of the high-spatial-resolution multispectral remote sensing images is 4, and the number of the low-spatial-resolution multispectral remote sensing images is 6.
The geometric registration comprises the following specific steps:
randomly selecting one high-spatial-resolution multispectral remote sensing image as a reference image;
automatically acquiring feature points of the high-spatial-resolution multispectral remote sensing image and the low-spatial-resolution multispectral remote sensing image by adopting an SIFT algorithm, screening the feature points by utilizing a first-order polynomial global change model, and calculating projection change model parameter estimation to obtain projection change parameters;
and performing geometric change and image interpolation on the low-spatial-resolution multispectral remote sensing image by using the projection change parameters to obtain an image after registration.
The spatial resampling specifically comprises:
and carrying out spatial resampling on the low spatial resolution multispectral remote sensing image by utilizing a bicubic convolution interpolation algorithm to obtain a resampled image, wherein the size and the pixels of the resampled image are the same as those of the high spatial resolution multispectral remote sensing image.
The "synthesizing the high spatial resolution multispectral remote sensing images to obtain a second panchromatic band image" specifically comprises: and obtaining a second panchromatic band image by adopting a mean synthesis mode on the basis of a plurality of high-spatial-resolution multispectral remote sensing images.
The formula for the mean synthesis is as follows:
in the above formula, bandiDenotes the ith band and n denotes the number of bands.
The phase recovery method is based on Gram-Schmidt algorithm, and the specific GS transformation formula is as follows:
in the formula GSTIs the T-th component, B, produced after GS transformationTIs the T wave band image u of the original multispectral remote sensing image with low spatial resolutionTIs the mean value of the gray values of the Tth original low spatial resolution multispectral remote sensing image.
The invention also provides an image fusion device of the remote sensing image, which comprises:
the acquisition module is used for respectively acquiring a plurality of high-spatial-resolution multispectral remote sensing images and n low-spatial-resolution multispectral remote sensing images; wherein n is more than or equal to 1;
the registration resampling module is used for carrying out geometric registration and resampling on the n low-spatial-resolution multispectral remote sensing images to obtain simulated low-spatial-resolution panchromatic band images;
the synthesis module is used for synthesizing the high-spatial-resolution multispectral remote sensing images into a high-spatial-resolution panchromatic waveband image;
a transformation module, configured to perform GS transformation on the original n low-spatial-resolution multispectral remote sensing images based on a phase recovery method by using the low-spatial-resolution panchromatic band image as a first component of the GS transformation, and output a plurality of GS-transformed band images, where an image corresponding to the low-spatial-resolution panchromatic band image is the GS-transformed first band image GS1And the images corresponding to the rest of the low-spatial-resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1；
The modification module is used for modifying the high-spatial-resolution panchromatic waveband image according to the first component of the GS conversion to obtain a modified image;
a fusion module for replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband images as first components of the GS inverse transformation, outputting n +1 GS inversely transformed waveband images, and removing the GS inversely transformed first waveband image corresponding to the first components of the GS inverse transformation to obtain a fused image.
The present invention also provides an electronic device comprising a processor, and a memory coupled to the processor, the memory storing program instructions executable by the processor; the processor, when executing the program instructions stored in the memory, implements the road network change detection method described above.
The present invention also provides a storage medium having stored therein program instructions which, when executed by a processor, implement the above-described road network change detection method.
According to the technical scheme, the image fusion method of the remote sensing image fuses the plurality of high-spatial-resolution multispectral remote sensing images and the plurality of low-spatial-resolution multispectral remote sensing images, can effectively improve the spatial resolution of the low-spatial-resolution multispectral remote sensing images, can keep the characteristics of the spectrum unchanged, and provides more effective data support for subsequent image application. A plurality of high-spatial-resolution multispectral remote sensing images are combined into a high-spatial-resolution panchromatic band image, more data can be provided for reference and correction, and therefore the fused image is higher in precision.
Drawings
Fig. 1 is a schematic flow chart of an image fusion method of remote sensing images according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an image fusion apparatus for remote sensing images according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flow chart of an image fusion method of remote sensing images according to a first embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same.
Example 1: as shown in fig. 1, the image fusion method for remote sensing images of the present embodiment includes the following steps:
s10, respectively obtaining a plurality of high spatial resolution multispectral remote sensing images and n low spatial resolution multispectral remote sensing images; wherein n is more than or equal to 1, and the value of n in the embodiment is 6.
In this embodiment, the image fusion method for remote sensing images obtains the multiple high spatial resolution multispectral remote sensing images and the multiple low spatial resolution multispectral remote sensing images based on a sentinel 2A/B satellite, wherein the number of the high spatial resolution multispectral remote sensing images is 4, and the multispectral remote sensing images are respectively a Band 2-blue Band, a Band 3-green Band, a Band 4-red Band and a Band 8-near-infrared Band in a sentinel 2A/B Band parameter table. That is, in the present embodiment, the multispectral remote sensing image with the spatial resolution equal to 10 meters is the multispectral remote sensing image with the high spatial resolution. The number of the low spatial resolution multispectral remote sensing images is 6, and the multispectral remote sensing images are respectively a Band 5-red edge wave Band, a Band 6-red edge wave Band, a Band 7-red edge wave Band, a Band 8A-narrow edge near infrared, a Band 11-short wave infrared and a Band 12-short wave infrared in a parameter table of a sentinel 2A/B wave Band. That is, in the present embodiment, the multispectral remote sensing image with the spatial resolution equal to 20 meters is a low spatial resolution multispectral remote sensing image.
And S20, performing geometric registration and resampling on the 6 low spatial resolution multispectral remote sensing images, and synthesizing the registered and resampled low spatial resolution multispectral remote sensing images to obtain a simulated first panchromatic waveband image.
S21, the geometric registration comprises the following specific steps:
and randomly selecting one high-spatial-resolution multispectral remote sensing image as a reference image.
And automatically acquiring the characteristic points of the high-spatial-resolution multispectral remote sensing image and the low-spatial-resolution multispectral remote sensing image by adopting an SIFT algorithm, screening the characteristic points by utilizing a first-order polynomial global change model, calculating projection change model parameter estimation, and obtaining projection change parameters.
And performing geometric change and image interpolation on the low-spatial-resolution multispectral remote sensing image by using the projection change parameters to obtain an image after registration.
S22, the spatial resampling specifically includes:
and carrying out spatial resampling on the low spatial resolution multispectral remote sensing image by utilizing a bicubic convolution interpolation algorithm to obtain a resampled image, wherein the size and the pixels of the resampled image are the same as those of the high spatial resolution multispectral remote sensing image.
The formula for the bicubic convolution interpolation algorithm is as follows:
wherein h represents a sampling interval, xkRepresenting interpolation points, u representing kernel for interpolation convolution, g being an interpolation function, ckIn order to depend on the parameters of the sample point data, they must satisfy g (x)k)＝f(xk) The conditions of (1).
S23, simulation: obtaining a first panchromatic band image by means of mean synthesis, wherein the mean synthesis formula is as follows:
in the above formula, bandiDenotes the ith band and n denotes the number of bands.
And S30, synthesizing the high-spatial-resolution multispectral remote sensing images to obtain a simulated second panchromatic waveband image.
And obtaining a second panchromatic band image by adopting a mean value synthesis mode, wherein the mean value synthesis formula is as follows:
in the above formula, bandiDenotes the ith band and n denotes the number of bands.
S40, based on a phase recovery method, taking the first panchromatic waveband image as a first component of GS conversion, performing GS conversion on the original n low-spatial-resolution multispectral remote sensing images, and outputting a plurality of GS-converted waveband images, wherein the image corresponding to the first panchromatic waveband image is the GS-converted first waveband image GS1And the images respectively corresponding to the rest of the low spatial resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1。
The phase recovery method is based on Gram-Schmidt algorithm, Schmidt orthogonalization is adopted, and the formula of the Schmidt orthogonalization is as follows:
v1＝u1；
……
wherein v is1、v2……vnAre mutually independent vectors; u. of1、u2……unFor constructed orthogonalityAnd (5) vector quantity.
The specific GS transform formula is as follows:
in the formula GSTIs the T-th component, B, produced after GS transformationTIs the T wave band image u of the original multispectral remote sensing image with low spatial resolutionTIs the mean value of the gray values of the Tth original low spatial resolution multispectral remote sensing image.
And S50, modifying the second panchromatic waveband image according to the first component of the GS conversion to obtain a modified image. Specifically, the mean value and the standard deviation of the second panchromatic band image and the mean value and the standard deviation of the GS1 are respectively calculated, and the second panchromatic band image is modified according to the two groups of data to obtain a modified image.
The specific modification formula is as follows:
k2＝uintensity-(k1×upan)；
wherein P is the gray scale value of the first panchromatic band image, eintensityIs the variance of the luminance component I, epanThe variance of the first panchromatic band image; u. ofintensityIs the mean value of the luminance components, upanIs the average gray level of the first panchromatic band image. k is a radical of1To gain, k2Is an offset.
S60, replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband videos as a first component of the GS inverse transformation, outputting n +1 GS inversely transformed waveband videos, and removing the GS inversely transformed first waveband video corresponding to the first component of the GS inverse transformation to obtain a fused video.
The specific GS inverse transformation formula is as follows:
in the formula, BTIs the T-th band image after GS inverse transformation, GSTIs the T-th component, u, produced after GS transformationTIs the mean value of the gray value of the T-th band image after GS conversion. GSlIndicating that the ith component after Gram-Schmidt transformation is matched by adjusting the statistics of the high resolution band image.
The image fusion method of the remote sensing image can effectively improve the spatial resolution of the original low-spatial-resolution multispectral remote sensing image while maintaining the spectral characteristics of the multispectral remote sensing image, and adopts a GS (generalized element) algorithm to fuse a plurality of low-spatial-resolution multispectral remote sensing images and a plurality of high-spatial-resolution multispectral remote sensing images, so that the spatial resolution is improved to be consistent with the spatial resolution of the high-spatial-resolution multispectral remote sensing images, and finally, high-quality remote sensing image data are provided for information extraction, quantitative parameter inversion and the like.
As shown in fig. 2, the present embodiment further provides an image fusion apparatus for remote sensing images, including:
the acquiring module 31 is configured to acquire a plurality of high spatial resolution multispectral remote sensing images and n low spatial resolution multispectral remote sensing images, respectively; wherein n is more than or equal to 1.
And the registration resampling module 32 is configured to perform geometric registration and resampling on the n low-spatial-resolution multispectral remote sensing images to obtain a simulated low-spatial-resolution panchromatic band image.
And the synthesis module 33 is configured to synthesize the high-spatial-resolution multispectral remote sensing images into a high-spatial-resolution panchromatic band image.
A transformation module 34, configured to perform GS transformation on the original n low-spatial-resolution multispectral remote sensing images by using the low-spatial-resolution panchromatic band image as a first component of the GS transformation based on a phase recovery method, and output a plurality of GS-transformed band images, where an image corresponding to the low-spatial-resolution panchromatic band image is the GS-transformed first band image GS1And the images corresponding to the rest of the low-spatial-resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1。
And a modifying module 35, configured to modify the high-spatial-resolution panchromatic band image according to the first component of the GS transform, so as to obtain a modified image.
A fusion module 36 for replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband images as first components of the GS inverse transformation, outputting n +1 GS inversely transformed waveband images, and removing the GS inversely transformed first waveband image corresponding to the first components of the GS inverse transformation to obtain a fused image.
As shown in fig. 3, the present embodiment further provides an electronic device 40, where the electronic device 40 includes a processor 41, and a memory 42 coupled to the processor 41, and the memory 42 stores program instructions executable by the processor 41; the processor 41 implements the road network change detection method described above when executing the program instructions stored in the memory 42.
As shown in fig. 4, the present embodiment further provides a storage medium 60, wherein the storage medium 60 stores program instructions 61, and the program instructions 61, when executed by a processor, implement the above-mentioned road network change detection method.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
While the foregoing is directed to embodiments of the present invention, it will be understood by those skilled in the art that various changes may be made without departing from the spirit and scope of the invention.
Claims (10)
1. An image fusion method of remote sensing images is characterized by comprising the following steps:
respectively obtaining a plurality of high spatial resolution multispectral remote sensing images and n low spatial resolution multispectral remote sensing images; wherein n is more than or equal to 1;
carrying out geometric registration and resampling on the n low spatial resolution multispectral remote sensing images, and synthesizing to obtain a simulated first panchromatic band image;
synthesizing the high-spatial-resolution multispectral remote sensing images to obtain a simulated second panchromatic band image;
based on a phase recovery method, taking the first panchromatic waveband image as a first component of GS conversion, performing GS conversion on the original n low-spatial-resolution multispectral remote sensing images, and outputting a plurality of GS-converted waveband images, wherein the image corresponding to the first panchromatic waveband image is the GS-converted first waveband image1And the images respectively corresponding to the rest of the low spatial resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1；
Modifying the second panchromatic waveband image according to the first component of the GS conversion to obtain a modified image;
replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband videos as a first component of the GS inverse transformation, outputting n +1 GS inversely transformed waveband videos, and removing the GS inversely transformed first waveband video corresponding to the first component of the GS inverse transformation to obtain a fused video.
2. The image fusion method for remote sensing images according to claim 1, characterized in that: the image fusion method of the remote sensing images is characterized in that a sentinel 2A/B satellite is used for obtaining a plurality of high-spatial-resolution multispectral remote sensing images and a plurality of low-spatial-resolution multispectral remote sensing images, wherein the number of the high-spatial-resolution multispectral remote sensing images is 4, and the number of the low-spatial-resolution multispectral remote sensing images is 6.
3. The image fusion method for remote sensing images according to claim 1, characterized in that: the geometric registration comprises the following specific steps:
randomly selecting one high-spatial-resolution multispectral remote sensing image as a reference image;
automatically acquiring feature points of the high-spatial-resolution multispectral remote sensing image and the low-spatial-resolution multispectral remote sensing image by adopting an SIFT algorithm, screening the feature points by utilizing a first-order polynomial global change model, and calculating projection change model parameter estimation to obtain projection change parameters;
and performing geometric change and image interpolation on the low-spatial-resolution multispectral remote sensing image by using the projection change parameters to obtain an image after registration.
4. The image fusion method for remote sensing images according to claim 1, characterized in that: the spatial resampling specifically comprises:
and carrying out spatial resampling on the low spatial resolution multispectral remote sensing image by utilizing a bicubic convolution interpolation algorithm to obtain a resampled image, wherein the size and the pixels of the resampled image are the same as those of the high spatial resolution multispectral remote sensing image.
5. The image fusion method for remote sensing images according to claim 1, characterized in that: the "synthesizing the high spatial resolution multispectral remote sensing images to obtain a second panchromatic band image" specifically comprises: and obtaining a second panchromatic band image by adopting a mean synthesis mode on the basis of a plurality of high-spatial-resolution multispectral remote sensing images.
6. The image fusion method for remote sensing images according to claim 5, characterized in that: the formula for the mean synthesis is as follows:
in the above formula, bandiDenotes the ith band and n denotes the number of bands.
7. The image fusion method for remote sensing images according to claim 1, characterized in that: the phase recovery method is based on Gram-Schmidt algorithm, and the specific GS transformation formula is as follows:
in the formula GSTIs the T-th component, B, produced after GS transformationTIs the T wave band image u of the original multispectral remote sensing image with low spatial resolutionTIs the mean value of the gray values of the Tth original low spatial resolution multispectral remote sensing image.
8. An image fusion device of remote sensing images is characterized in that: comprises that
The acquisition module is used for respectively acquiring a plurality of high-spatial-resolution multispectral remote sensing images and n low-spatial-resolution multispectral remote sensing images; wherein n is more than or equal to 1;
the registration resampling module is used for carrying out geometric registration and resampling on the n low-spatial-resolution multispectral remote sensing images to obtain simulated low-spatial-resolution panchromatic band images;
the synthesis module is used for synthesizing the high-spatial-resolution multispectral remote sensing images into a high-spatial-resolution panchromatic waveband image;
a transformation module, configured to perform GS transformation on the original n low-spatial-resolution multispectral remote sensing images based on a phase recovery method by using the low-spatial-resolution panchromatic band image as a first component of the GS transformation, and output a plurality of GS-transformed band images, where an image corresponding to the low-spatial-resolution panchromatic band image is the GS-transformed first band image GS1And the images corresponding to the rest of the low-spatial-resolution multispectral remote sensing images are subsequent waveband images GS after GS conversion2、GS3……GSn+1；
The modification module is used for modifying the high-spatial-resolution panchromatic waveband image according to the first component of the GS conversion to obtain a modified image;
a fusion module for replacing the GS with the modified image1And performing GS inverse transformation on the plurality of GS transformed waveband images as first components of the GS inverse transformation, outputting n +1 GS inversely transformed waveband images, and removing the GS inversely transformed first waveband image corresponding to the first components of the GS inverse transformation to obtain a fused image.
9. An electronic device comprising a processor, and a memory coupled to the processor, the memory storing program instructions executable by the processor, wherein: said processor implementing said road network change detection method according to any one of claims 1 to 7 when executing said program instructions stored in said memory.
10. A storage medium, characterized by: the storage medium having stored therein program instructions which, when executed by a processor, implement a road network change detection method according to any one of claims 1 to 7.
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| CN116977868A (en) * | 2023-06-07 | 2023-10-31 | 珠江水利委员会珠江水利科学研究院 | Image product fusion method, system and storage medium based on feature matching |
| CN117197625A (en) * | 2023-08-29 | 2023-12-08 | 珠江水利委员会珠江水利科学研究院 | Remote sensing image space-spectrum fusion method, system, equipment and medium based on correlation analysis |
| CN117253125A (en) * | 2023-10-07 | 2023-12-19 | 珠江水利委员会珠江水利科学研究院 | Space-spectrum mutual injection image fusion method, system and readable storage medium |
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