CN114994675B - Glacier classification method and system based on normalized intensity dispersion index - Google Patents
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Abstract
The application provides a glacier classification method and system based on normalized intensity dispersion indexes, and belongs to the technical field of glacier classification. The method comprises the following steps: performing multi-temporal filtering processing on the sequential SAR multi-view intensity image; performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation; calculating to obtain normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment; glaciers are classified using threshold segmentation. According to the application, the stability of the surface backscattering coefficient in the glacier action area is modeled based on the normalized intensity dispersion index NIDI, and the information contrast of the glacier and the non-glacier surface is enhanced by quantifying the stability of the backscattering of the glacier and the non-glacier surface, so that a standard index base is provided for glacier classification, the contrast of the glacier and the adjacent non-glacier surface can be enhanced, and the glacier classification difficulty is reduced.
Description
Technical Field
The application belongs to the technical field of glacier classification, and particularly relates to a glacier classification method and system based on normalized intensity dispersion indexes.
Background
In the last 80 th century, aerial photogrammetry images and mapping results thereof including digital elevation models DEM, orthographic images, topography maps and the like become main data sources for regional glacier editing. With the free open policy of the United States Geological Survey (USGS) for Landsat earth observation satellite data in 2008, the remote sensing technology plays an increasingly important role in glacier resource investigation and dynamic evolution monitoring thereof. With the supplementation of resolution satellite data in ASTER series, the Goinby program Sentinel-2A/B constellation and the like, the remote sensing technology has become an extremely important data support for frozen circle ground observation. The second glacier cataloging plan started in China in 2014, the satellite-borne multispectral remote sensing images (ASTER, landsat series) become main data sources for glacier identification and boundary extraction, and aerial images are only used as supplementary data in warm season cloud and fog coverage areas in southeast areas of Tibetan.
Ice and snow has strong absorption characteristics in the short wave infrared band (1.55-1.75 μm) and exhibits strong reflection characteristics in the visible to infrared band (0.45-0.90 μm). Based on the above, students at home and abroad propose to improve the efficiency and the precision of glacier identification by enhancing the contrast between glacier coverage area and surrounding environment on the basis of band conversion. Such as a band ratio method and a normalized snow index (Normalized Difference Snow Index, NDSI) model, wherein the former uses the contrast characteristics of ice and snow absorption and reflection in different bands to amplify the signal of an ice and snow area through the same-name pixel ratio; the latter uses normalized band transforms to highlight ice and snow information.
For clean glaciers, although solar light shadows, water bodies such as glaciers and the like can interfere classification of the glaciers in mountainous areas with complex terrains, the glaciers and other ground objects have obvious spectrum differences, and the glaciers and other ground objects can be effectively separated after the wave band ratio and the wave band are converted. For the surface-covered glacier, since the tillite is mainly composed of stones and gravel chips falling from slopes on two sides of the glacier, when the glacier is covered by the tillite, the glacier surface has similar spectral characteristics with the surrounding environment, and the assumption of classifying the glacier and the surrounding ground based on spectral transformation is not applicable any more. Therefore, scholars at home and abroad try other methods or assist other information (such as topography factors) to develop the automatic classification of the moraine covered glaciers, such as an artificial neural network method, thermal infrared remote sensing assistance and the like. However, most automatic classification methods have problems that are difficult to apply in a wide range due to the thickness of the moraine and the homogeneity of the moraine covering glaciers and surrounding ground objects.
The advantage of the Synthetic Aperture Radar (SAR) that it can penetrate the cloud can be used as an alternative or complement to the optical remote sensing image, however, due to the lack of texture features required for glacier interpretation of the SAR image, in particular the SAR multi-view intensity image (multi-looked intensity image, MLI), has difficulty in providing enough information for glacier interpretation due to the fact that only the scattering intensity information after the interaction of the incident electromagnetic wave and the earth surface is reflected; in addition, the inherent speckle noise of the SAR image forms serious interference on the identification of the glacier target, and the identification and extraction of the glacier boundary by directly using the SAR image are basically difficult to realize.
Disclosure of Invention
Aiming at the defects in the prior art, the glacier classification method and system based on the normalized intensity dispersion index provided by the application solve the problem that the contrast ratio between glaciers and adjacent glacier ground surfaces cannot be enhanced in the prior art, so that the difficulty of glacier classification is high.
In order to achieve the above purpose, the application adopts the following technical scheme:
the scheme provides a glacier classification method based on normalized intensity dispersion indexes, which comprises the following steps:
s1, registering and multi-view preprocessing are carried out on a sequential SAR image, and multi-temporal filtering processing is carried out on the sequential SAR multi-view intensity image to obtain a stacked sequential SAR multi-view intensity image;
s2, performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation;
s3, calculating to obtain normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment;
s4, classifying the glaciers by using threshold segmentation based on normalized intensity dispersion indexes of the glaciers and the non-glacier ground surfaces.
The beneficial effects of the application are as follows: SAR image backscattering coefficients caused by rapid motion and seasonal ablation of glacier surfaces have stronger discrete characteristics, normalized intensity dispersion index NIDI is the characteristic, modeling is conducted on the stability of the surface backscattering coefficients in a glacier action area, and information contrast of glacier and non-glacier surface backscattering is further enhanced by quantifying the stability of the glacier and non-glacier surface backscattering, so that a standard index is provided for glacier classification, the contrast of glacier and adjacent non-glacier surfaces can be enhanced, and the glacier classification difficulty is reduced.
Further, the step S1 includes the steps of:
s101, performing coarse registration on a time-series SAR image by using a pixel matching method, and performing fine registration by using intensity information of the SAR image and a pixel offset method;
s102, acquiring a sequential SAR multi-view intensity image according to the precisely registered sequential SAR image by using a unified multi-view coefficient;
s103, performing multi-temporal filtering processing on the sequential SAR multi-view intensity image to obtain a stacked sequential SAR multi-view intensity image.
The beneficial effects of the above-mentioned further scheme are: the application uses the pixel offset technology to iterate, thereby continuously updating the registration polynomial and enabling the registration precision of the sequential SAR image to reach a reasonable range.
Still further, the step S2 includes the steps of:
s201, calculating the maximum natural estimation distribution of a time domain median value and a sample standard deviation by using a stacked time sequence SAR multi-view intensity image sequence;
s202, estimating confidence interval upper bound of confidence according to maximum likelihood estimation distribution of the time domain median and sample standard deviationAnd lower boundaryp；
S203, with confidence interval upper boundAnd lower boundarypAs a threshold value, filtering out a time sequence SAR multi-view intensity image pixel { p } i |p 1 ,p 2 ,...,p n P in } i <pOr->Abnormal pixel of (1), wherein p i Representing a time sequence SAR multi-view intensity image pixel aggregation set, wherein pn represents an nth time sequence SAR multi-view intensity image pixel, i=1, 2,.. N, i represents an intensity value of an ith time sequence SAR multi-view intensity image, and n represents the number of pixels before eliminating the intensity abnormal value of the time sequence SAR multi-view intensity image;
s204, judging time sequence SAR multi-view intensity image pixel { p } i |p 1 ,p 2 ,...,p n If all abnormal pixels in the image are filtered out, the step S205 is carried out, otherwise, the step S201 is returned;
s205, judging whether all time domain abnormal values of all pixels in the time sequence SAR multi-view intensity image are filtered, if yes, entering a step S3, otherwise, returning to the step S201.
The beneficial effects of the above-mentioned further scheme are: and screening and removing abnormal values by using statistical information of each pixel in the time domain, so as to refine the SAR image intensity value in the time domain.
Still further, the step S3 includes the steps of:
s301, calculating to obtain a time domain mean value according to the time sequence SAR multi-view intensity image subjected to time domain filtering;
s302, calculating to obtain a time domain sample standard deviation according to a time sequence SAR multi-view intensity image subjected to time domain filtering treatment;
s303, calculating to obtain a normalized intensity dispersion index according to the time domain mean value and the time domain sample standard deviation.
The beneficial effects of the above-mentioned further scheme are: according to the application, the standard deviation and the time domain mean value of the time domain sample of each pixel on the time domain are calculated by using the SAR multi-view intensity image of the refined stack, and the normalized intensity value dispersion index is calculated on the basis, so that the information contrast ratio of glaciers and non-glaciers is enhanced.
Still further, the expression of the time domain mean value in the step S301 is as follows:
wherein μ (r, c) represents a time domain mean value, k represents the number of pixels reserved after the removal of the intensity outlier of the sequential SAR multi-view intensity image corresponding to (r, c) coordinates, n represents the number of pixels before the removal of the intensity outlier of the sequential SAR multi-view intensity image, and p i And (5) representing the intensity value of the ith time sequence SAR multi-view intensity image.
The beneficial effects of the above-mentioned further scheme are: the stacked SAR image average intensity map is obtained, and an input data result is provided for step S303.
Still further, the expression of the standard deviation of the time domain samples in the step S302 is as follows:
where delta (r, c) represents the standard deviation of the time domain samples,indicating the upper bound of the confidence interval.
The beneficial effects of the above-mentioned further scheme are: the standard deviation of the stacked SAR image intensity map is obtained, and input data is provided for step S303.
Still further, the expression of the normalized intensity dispersion index in step S303 is as follows:
NIDI(r,c)＝1-abs(δ(r,c)-μ(r,c))/(δ(r,c)-μ(r,c))
where NIDI (r, c) represents the normalized intensity dispersion index and abs (. Cndot.) represents the absolute value.
The beneficial effects of the above-mentioned further scheme are: and calculating a normalized intensity value dispersion index, so that the information contrast of glaciers and non-glaciers is enhanced.
Based on the method, the application also provides a glacier classification system based on the normalized intensity dispersion index, which comprises the following steps:
the preprocessing module is used for registering and multi-view preprocessing the sequential SAR images and performing multi-temporal filtering processing on the sequential SAR multi-view intensity images to obtain stacked sequential SAR multi-view intensity images;
the time domain filtering module is used for performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation;
the calculation module is used for calculating and obtaining normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment;
and the classification module is used for classifying the glaciers by using threshold segmentation based on the normalized intensity dispersion indexes of the glacier and the non-glacier ground surface.
The beneficial effects of the application are as follows: SAR image backscattering coefficients caused by rapid motion and seasonal ablation of glacier surfaces have stronger discrete characteristics, normalized intensity dispersion index NIDI is the characteristic, modeling is conducted on the stability of the surface backscattering coefficients in a glacier action area, and information contrast of glacier and non-glacier surface backscattering is further enhanced by quantifying the stability of the glacier and non-glacier surface backscattering, so that a standard index is provided for glacier classification, the contrast of glacier and adjacent non-glacier surfaces can be enhanced, and the glacier classification difficulty is reduced.
Drawings
FIG. 1 is a flow chart of the method of the present application.
Fig. 2 is a graph of normalized intensity dispersion index before correction in this example.
Fig. 3 is a graph of normalized intensity dispersion index after correction in this example.
Fig. 4 is a graph showing the normalized intensity dispersion index residual distribution before and after correction in this example.
Fig. 5 is a histogram corresponding to fig. 2, 3 and 4, respectively, in the present embodiment.
Fig. 6 is a schematic diagram of a system structure according to the present application.
Detailed Description
The following description of the embodiments of the present application is provided to facilitate understanding of the present application by those skilled in the art, but it should be understood that the present application is not limited to the scope of the embodiments, and all the applications which make use of the inventive concept are protected by the spirit and scope of the present application as defined and defined in the appended claims to those skilled in the art.
Example 1
As shown in fig. 1, the application provides a glacier classification method based on normalized intensity dispersion indexes, which is implemented as follows:
s1, registering and multi-view preprocessing are carried out on a sequential SAR image, multi-temporal filtering processing is carried out on the sequential SAR multi-view intensity image, and a stacked sequential SAR multi-view intensity image is obtained, and the implementation method is as follows:
s101, performing coarse registration on a time-series SAR image by using a pixel matching method, and performing fine registration by using intensity information of the SAR image and a pixel offset method;
s102, acquiring a sequential SAR multi-view intensity image according to the precisely registered sequential SAR image by using a unified multi-view coefficient;
s103, performing multi-temporal filtering processing on the sequential SAR multi-view intensity image to obtain a stacked sequential SAR multi-view intensity image.
In the embodiment, firstly, coarse registration is performed on a time sequence SAR image through a pixel matching technology, and then fine registration is performed through a pixel offset technology by utilizing intensity information of the SAR image to obtain a fine registration offset polynomial; and acquiring the sequential SAR multi-view intensity image by using the accurate registered sequential SAR image and using a unified multi-view coefficient.
In this embodiment, the pixel offset technique is mainly used for iteration, so that the registration polynomial is continuously updated, and the registration accuracy reaches a reasonable range.
S2, performing time domain filtering processing on the stacked time sequence SAR multi-view intensity image through interval estimation, wherein the implementation method comprises the following steps of:
s201, calculating the maximum natural estimation distribution of a time domain median value and a sample standard deviation by using a stacked time sequence SAR multi-view intensity image sequence;
s202, estimating confidence interval upper bound of confidence according to maximum likelihood estimation distribution of the time domain median and sample standard deviationAnd lower boundaryp；
S203, with confidence interval upper boundAnd lower boundarypAs a threshold value, filtering out a time sequence SAR multi-view intensity image pixel { p } i |p 1 ,p 2 ,...,p n P in } i <pOr->Abnormal pixel of (1), wherein p i Representing a sequential SAR multi-view intensity image pixel aggregation set, p n Representing pixels of an nth time sequence SAR multi-view intensity image, wherein i=1, 2, & gt, n, i represents intensity values of the ith time sequence SAR multi-view intensity image, and n represents the number of pixels before eliminating the intensity abnormal values of the time sequence SAR multi-view intensity image;
s204, judging time sequence SAR multi-view intensity image pixel { p } i |p 1 ,p 2 ,...,p n If all abnormal pixels in the image are filtered out, the step S205 is carried out, otherwise, the step S201 is returned;
s205, judging whether all time domain abnormal values of all pixels in the time sequence SAR multi-view intensity image are filtered, if yes, entering a step S3, otherwise, returning to the step S201.
In this embodiment, due to the influence of factors such as atmospheric delay, surface humidity variation, and speckle noise, NIDI distribution similar to that of main glaciers exists on non-glacier surfaces and even glacier bordered surfaces, which weakens robustness of classifying glaciers based on NIDI and even leads to misclassification. For N stacked SAR intensity image sequences (M 1 ,M 2 ,…,M n ) Let P be (r,c) Time series of point intensity values { p } 1 ,p 2 ,...,p n }. P without regard to climate change and speckle noise (r,c) The pixels are mutually independent and integrally subject to normal distribution. Estimated pair { p over interval i |p 1 ,p 2 ,...,p n The abnormal value of the sample is judged, and the steps are as follows:
calculating P by using stacked SAR image multi-view intensity sequence (r,c) Pixel sequence { p } i |p 1 ,p 2 ,...,p n A median v and a maximum likelihood estimate of the sample standard deviation sigma; confidence interval upper bound with estimated 95% confidence for the calculated median v and sample standard deviation sigmaAnd lower boundarypThe method comprises the steps of carrying out a first treatment on the surface of the Above (I)>And lower boundarypFiltering { p for threshold value i |p 1 ,p 2 ,...,p n P in } i <pOr (b)Is not limited to, i.e.:
P′ (r,c) ＝{p i |p 1 ,p 2 ,...,p k },k≤n
wherein ,P′(r,c) Representing the time sequence of pixels after outlier rejection, P (r,c) Representing the original temporal sequence of picture elements up to P' (r,c) The number of the middle pixels is stable until the time domain outliers of all pixels are completely removed.
S3, calculating normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image subjected to time domain filtering treatment, wherein the implementation method comprises the following steps of:
s301, calculating to obtain a time domain mean value according to the time sequence SAR multi-view intensity image subjected to time domain filtering;
s302, calculating to obtain a time domain sample standard deviation according to a time sequence SAR multi-view intensity image subjected to time domain filtering treatment;
s303, calculating to obtain a normalized intensity dispersion index according to the time domain mean value and the time domain sample standard deviation.
In this embodiment, a time domain mean value is calculated based on the refined SAR multi-view intensity image, and a time domain intensity value sequence { p } of the time-sequence SAR multi-view intensity image coordinates (r, c) is calculated i |p 1 ,p 2 ,...,p n Sequence { p } after outlier removal i |p 1 ,p 2 ,...,p k K is less than or equal to n }, and the pixel time domain average value mu:
mu (r, c) represents a time domain mean value of the time sequence SAR multi-view intensity image coordinates (r, c), k represents the number of pixels reserved after the time sequence SAR multi-view intensity image intensity abnormal value corresponding to the time sequence SAR multi-view intensity image coordinates (r, c) is removed, n represents the number of pixels before the time sequence SAR multi-view intensity image intensity abnormal value corresponding to the time sequence SAR multi-view intensity image coordinates (r, c) is removed, and pi represents the intensity value of the ith time sequence SAR multi-view intensity image.
In this embodiment, the time-domain sample standard deviation is calculated based on the refined SAR multiview intensity image. Time domain intensity value sequence { p } for time-series SAR multi-view intensity image coordinates (r, c) i |p 1 ,p 2 ,...,p n Sequence { p } after outlier removal i |p 1 ,p 2 ,...,p k K is less than or equal to n, and the standard deviation delta (r, c) of the pixel time domain sample is:
wherein delta (r, c) represents the time domain sample standard deviation of the time sequence SAR multi-view intensity image coordinates (r, c), and p represents the confidence interval upper bound.
In this embodiment, the normalized intensity dispersion index NIDI is calculated based on the time domain mean and the time domain sample standard deviation:
NIDI(r,c)＝1-abs(δ(r,c)-μ(r,c))/(δ(r,c)-μ(r,c))
wherein NIDI (r, c) represents a normalized intensity dispersion index of the sequential SAR multiview intensity image coordinates (r, c), and abs (·) represents an absolute value.
Until all pixel normalized intensity dispersion index NIDI calculations are completed.
S4, classifying the glaciers by using threshold segmentation based on normalized intensity dispersion indexes of the glaciers and the non-glacier ground surfaces.
In this embodiment, in order to verify the effect of acquiring the normalized intensity dispersion index based on the sequential SAR image and facing the glacier classification, 29 Sentinel-1A satellite orbital C-band SAR images covering the imaging of the tribute shandong slope conch glacier and the mill channel glacier from 2018 month 1 to 12 month are selected as experimental data, and the abnormal values are filtered pixel by pixel for the stacked sequential SAR images by the above method. As shown in fig. 2 to 5, the distribution of NIDI and residual values of the experimental zone before and after correction and the corresponding statistical histograms thereof are shown, and it is apparent from the graphs that the contrast between glacier and non-glacier is significantly enhanced in the NIDI graph. There are multiple regions of high level NIDI in non-glacier regions prior to filtering out timing anomaly pixels, with portions distributed on the surface of the glacier adjacent to the glacier. In contrast to fig. 2, 3 and 4, significant high-level NIDI pixels in the non-glacier region of the corrected NIDI profile are substantially completely filtered. Fig. 5 shows the distribution of correction differences before and after correction, mainly around the ablation zone of the conch glacier. Notably, in fig. 2 and 3, the black oval dashed line marks the rim area around the bottom of the conch glacier and upstream of the glacier tongue, the ADI value range similar to the adjacent glacier is difficult to define before uncorrected glacier boundary profile, and the NIDI pel value and contrast of the glacier area corresponding after correction are significantly enhanced. In short, NIDI can effectively strengthen glacier and adjacent non-glacier ground surface's information contrast, reduces the degree of difficulty of classifying glacier based on SAR image and improves glacier classification's precision.
Example 2
As shown in fig. 6, the present application provides a glacier classification system based on a normalized intensity dispersion index, comprising:
the preprocessing module is used for registering and multi-view preprocessing the sequential SAR images and performing multi-temporal filtering processing on the sequential SAR multi-view intensity images to obtain stacked sequential SAR multi-view intensity images;
the time domain filtering module is used for performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation;
the calculation module is used for calculating and obtaining normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment;
and the classification module is used for classifying the glaciers by using threshold segmentation based on the normalized intensity dispersion indexes of the glacier and the non-glacier ground surface.
The glacier classification system based on the normalized intensity dispersion index provided in the embodiment shown in fig. 6 may execute the technical scheme shown in the glacier classification method based on the normalized intensity dispersion index in the embodiment of the method, and the implementation principle is similar to the beneficial effect, and is not repeated here.
In the embodiment of the application, the functional units can be divided according to the glacier classification method based on the normalized intensity dispersion index, for example, each function can be divided into each functional unit, and two or more functions can be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that the division of the units in the present application is schematic, only one logic division, and other division manners may be implemented in practice.
In the embodiment of the application, in order to realize the principle and the beneficial effect of the glacier classification method based on the normalized intensity dispersion index, the glacier classification system based on the normalized intensity dispersion index comprises a hardware structure and/or a software module for executing corresponding functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein are capable of being implemented as a combination of hardware and/or hardware and computer software, where a function is performed in either a hardware or a computer software driven manner, where different methods may be employed to implement the described function for each particular application depending upon the specific application and design constraints, but such implementation is not to be considered beyond the scope of the present application.
Claims (6)
1. The glacier classification method based on the normalized intensity dispersion index is characterized by comprising the following steps of:
s1, registering and multi-view preprocessing are carried out on a sequential SAR image, and multi-temporal filtering processing is carried out on the sequential SAR multi-view intensity image to obtain a stacked sequential SAR multi-view intensity image;
s2, performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation;
the step S2 includes the steps of:
s201, calculating maximum likelihood estimation distribution of a time domain median value and a sample standard deviation by using a stacked time sequence SAR multi-view intensity image sequence;
s202, estimating a confidence interval upper bound of the confidence coefficient according to the maximum likelihood estimation distribution of the time domain median and the sample standard deviationAnd lower bound->；
S203, with confidence interval upper boundAnd lower bound->Filtering out sequential SAR multi-view intensity image pixels with threshold valueMiddle->Or->Wherein,p i representing a sequential SAR multi-view intensity image pixel set,p n represent the firstnMultiple visual intensity image pixels of a time sequence SAR,>，ithe order of the intensity values of the stacked temporal SAR multiview images is represented,nrepresenting the number of pixels before eliminating the abnormal value of the intensity of the sequential SAR multi-view intensity image;
s204, judging time sequence SAR multi-view intensity image pixelsAbnormal image of middle schoolIf all the elements are filtered, the step S205 is entered, otherwise, the step S201 is returned;
s205, judging whether all time domain abnormal values of all pixels in the sequential SAR multi-view intensity image are filtered, if yes, entering a step S3, otherwise, returning to the step S201;
s3, calculating to obtain normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment;
s4, classifying the glaciers by using threshold segmentation based on normalized intensity dispersion indexes of the glaciers and the non-glacier ground surfaces.
2. The glacier classification method based on the normalized intensity dispersion index according to claim 1, wherein the step S1 includes the steps of:
s101, performing coarse registration on a time-series SAR image by using a pixel matching method, and performing fine registration by using intensity information of the SAR image and a pixel offset method;
s102, acquiring a sequential SAR multi-view intensity image according to the precisely registered sequential SAR image by using a unified multi-view coefficient;
s103, performing multi-temporal filtering processing on the sequential SAR multi-view intensity image to obtain a stacked sequential SAR multi-view intensity image.
3. The glacier classification method based on the normalized intensity dispersion index according to claim 2, wherein the step S3 includes the steps of:
s301, calculating to obtain a time domain mean value according to the time sequence SAR multi-view intensity image subjected to time domain filtering;
s302, calculating to obtain a time domain sample standard deviation according to a time sequence SAR multi-view intensity image subjected to time domain filtering treatment;
s303, calculating to obtain a normalized intensity dispersion index according to the time domain mean value and the time domain sample standard deviation.
4. The glacier classification method based on the normalized intensity dispersion index according to claim 3, wherein the expression of the time domain mean value in step S301 is as follows:
wherein ,representing the time-domain mean value,krepresenting the time sequence SAR multi-view intensity image coordinatesr,c) The number of pixels reserved after eliminating the intensity outlier of the corresponding sequential SAR multi-view intensity image，nRepresenting the number of pixels before eliminating the abnormal value of the intensity of the sequential SAR multi-view intensity image,p j representing the stack after outlier rejectionjThe amplitude sequence SAR looks at the intensity values of the intensity images.
5. The glacier classification method based on the normalized intensity dispersion index according to claim 4, wherein the expression of the time-domain sample standard deviation in step S302 is as follows:
wherein ,represents the standard deviation of the time domain samples, +.>Indicating the upper bound of the confidence interval.
6. A glacier classification system based on a normalized intensity dispersion index, comprising:
the preprocessing module is used for registering and multi-view preprocessing the sequential SAR images and performing multi-temporal filtering processing on the sequential SAR multi-view intensity images to obtain stacked sequential SAR multi-view intensity images;
the time domain filtering module is used for performing time domain filtering processing on the stacked time sequence SAR multi-view intensity images through interval estimation, and specifically comprises the following steps:
a1, calculating maximum likelihood estimation distribution of a time domain median value and a sample standard deviation by using a stacked time sequence SAR multi-view intensity image sequence;
a2, estimating a confidence interval upper bound of the confidence coefficient according to the maximum likelihood estimation distribution of the time domain median and the sample standard deviationAnd lower bound->；
A3, with confidence interval upper boundAnd lower bound->Filtering out sequential SAR multi-view intensity image pixels with threshold valueMiddle->Or->Wherein,p i representing a sequential SAR multi-view intensity image pixel set,p n represent the firstnMultiple visual intensity image pixels of a time sequence SAR,>，ithe order of the intensity values of the stacked temporal SAR multiview images is represented,nrepresenting the number of pixels before eliminating the abnormal value of the intensity of the sequential SAR multi-view intensity image;
a4, judging time sequence SAR multi-view intensity imagePixel elementIf all abnormal pixels in the image are filtered, the step A5 is carried out, otherwise, the step A1 is returned;
a5, judging whether all time domain abnormal values of all pixels in the sequential SAR multi-view intensity image are filtered, if yes, executing a calculation module, otherwise, returning to the step A1;
the calculation module is used for calculating and obtaining normalized intensity dispersion indexes of glacier and non-glacier ground surfaces according to the time sequence SAR multi-view intensity image after time domain filtering treatment;
and the classification module is used for classifying the glaciers by using threshold segmentation based on the normalized intensity dispersion indexes of the glacier and the non-glacier ground surface.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9354308B1 (en) * | 2014-12-05 | 2016-05-31 | Trimble Navigation Limited | Micro climate corrections for radar interferometry measurements |
| CN110456352A (en) * | 2019-08-30 | 2019-11-15 | 南京大学 | A kind of glacier recognition methods based on coherence factor threshold value |
| CN110516646A (en) * | 2019-09-02 | 2019-11-29 | 南京大学 | A kind of superglacial moraine cover type glacier recognition methods of combination polarization decomposing and features of terrain |
| CN112034461A (en) * | 2020-09-01 | 2020-12-04 | 国网山西省电力公司阳泉供电公司 | Synthetic measure-based SAR image PS point selection method |
| CN113822141A (en) * | 2021-07-28 | 2021-12-21 | 青海省地质调查院(青海省地质矿产研究院、青海省地质遥感中心) | Automatic glacier and snow extraction method and system based on remote sensing image |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0701869D0 (en) * | 2007-01-31 | 2007-03-14 | Cambridge Consultants | Adaptive radar |
| US10037477B2 (en) * | 2015-08-31 | 2018-07-31 | Massachusetts Institute Of Technology | Combined intensity and coherent change detection in images |
-
2022
- 2022-04-19 CN CN202210411508.5A patent/CN114994675B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9354308B1 (en) * | 2014-12-05 | 2016-05-31 | Trimble Navigation Limited | Micro climate corrections for radar interferometry measurements |
| CN110456352A (en) * | 2019-08-30 | 2019-11-15 | 南京大学 | A kind of glacier recognition methods based on coherence factor threshold value |
| CN110516646A (en) * | 2019-09-02 | 2019-11-29 | 南京大学 | A kind of superglacial moraine cover type glacier recognition methods of combination polarization decomposing and features of terrain |
| CN112034461A (en) * | 2020-09-01 | 2020-12-04 | 国网山西省电力公司阳泉供电公司 | Synthetic measure-based SAR image PS point selection method |
| CN113822141A (en) * | 2021-07-28 | 2021-12-21 | 青海省地质调查院(青海省地质矿产研究院、青海省地质遥感中心) | Automatic glacier and snow extraction method and system based on remote sensing image |
Non-Patent Citations (8)
| Title |
|---|
| Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images;Li Fang , Yusheng Xu, Wei Yao, Uwe Stilla;《ISPRS Journal of Photogrammetry and Remote Sensing》;92-112 * |
| InSAR时序分析高相干目标选取方法比较研究;范锐彦;焦健;高胜;曾琪明;;地球信息科学学报(第06期);805-814 * |
| Landsat 8 OLI影像的高原山地阴影区冰川识别方法;季漩;陈云芳;罗贤;李运刚;;光谱学与光谱分析(第12期);全文 * |
| Monitoring Dynamic Evolution of the Glacial Lakes by Using Time Series of Sentinel-1A SAR Images;Bo Zhang，Guoxiang Liu;《 Remote Sens. 》;1-21 * |
| 地表形变D-InSAR监测方法及关键问题分析;葛大庆;王艳;范景辉;刘圣伟;郭小方;王毅;;国土资源遥感(第04期);14-22 * |
| 张波 ; 张瑞 ; 刘国祥 ; 刘巧 ; 蔡嘉伦.基于SAR影像的贡巴冰川末端冰湖年际变化监测及溃决规律分析.《武汉大学学报(信息科学版)》.2019,1054-1064. * |
| 雷达遥感影像在水稻信息提取及估产方面的技术研究;黄晓军;李秉柏;;江苏农业科学(第06期);全文 * |
| 黄磊 ; 李震 ; 周建民 ; 田帮森.SAR监测冰川变化研究进展.《地球科学进展》.2014,985-994. * |
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