CN118363007B - Tidal flat terrain time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction - Google Patents
Tidal flat terrain time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction Download PDFInfo
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Abstract
The application discloses a tidal flat topography time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction. Firstly, acquiring tidal flat inundation information based on a time sequence Synthetic Aperture Radar (SAR) remote sensing image; the tidal flat remote sensing submerged frequency correction is realized by reconstructing the image time sequence, and the consistency of the tidal flat remote sensing submerged frequencies of different time sequences is improved; and then combining the corrected tidal flat remote sensing inundation frequency with laser satellite height measurement data, and establishing a tidal flat terrain inversion model suitable for any monitoring time sequence based on a machine learning method to complete the tidal flat time sequence monitoring of the target area. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing submerged frequency correction constructed by the application eliminates the deviation of the tidal flat remote sensing submerged frequency on time sequence caused by the difference of the tidal level distribution during remote sensing, improves the time sequence consistency of the tidal flat remote sensing submerged frequency, constructs a tidal flat topography inversion model suitable for any monitoring time sequence based on the time sequence, and realizes the monitoring of the tidal flat topography time sequence.
Description
Technical Field
The application belongs to the technical field of coastal wetland remote sensing monitoring, and relates to a tidal flat topography time sequence monitoring method. Specifically, the application provides a tidal flat topography time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction.
Background
The tidal flat topography time sequence monitoring is an important component part of the development and management of coastal zone resources, and plays an important role in guaranteeing the stability of the ecological environment of the coastal zone and promoting the sustainable development of the coastal zone economy. The tidal flat is an important transition zone between the sea and the land, has extremely high ecological value, and has important effects of maintaining biodiversity, weakening the influence of extreme storm surge, coping with the rising of sea level and the like. Therefore, there is a need to develop a tidal flat topography time sequence dynamic monitoring method, develop an effective monitoring of tidal flat topography and dynamic evolution thereof, and provide data support and scientific basis for the protection, recovery or reconstruction of tidal flat.
The remote sensing method is widely used for monitoring the topography of the tidal flat in a large range due to the advantages of the remote sensing technology such as large range, high timeliness and low cost and the existence of a large amount of historical archived data. The submerged frequency method is an important means for acquiring tidal flat terrain based on satellite remote sensing. The tidal flat inundation frequency method obtains the tidal flat inundation frequency through time sequence remote sensing image inversion, then establishes a quantitative relation between the tidal flat inundation frequency and the tidal flat elevation, and converts the tidal flat inundation frequency into the tidal flat elevation. However, the time sequence monitoring capability of the method is insufficient, and because the tidal flat remote sensing inundation frequency in different periods is influenced by the remote sensing observation quantity and the tidal level distribution difference, the relationship between the tidal flat inundation frequency and the tidal flat elevation is difficult to obtain uniformly, so that the model constructed in a certain period is difficult to popularize and apply to the tidal flat topography monitoring in other periods. While there is often difficulty in constructing a tidal flat flooding frequency-tidal flat elevation relationship model for each time period due to the lack of a priori data.
Based on the method, the application provides a tidal flat terrain time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction. Aiming at the requirements of tidal flat topography time sequence monitoring, the application fully utilizes the time sequence Synthetic Aperture Radar (SAR) remote sensing image, realizes the tidal flat remote sensing submerged frequency correction by reconstructing the image time sequence, and solves the limitations and inefficiency of the traditional tidal flat topography remote sensing detection method in time sequence monitoring.
Disclosure of Invention
The application aims to provide a tidal flat topography time sequence monitoring method and a system based on tidal flat remote sensing inundation frequency correction, which eliminate the deviation of the tidal flat remote sensing inundation frequency on time sequence caused by the difference of the tidal level distribution during remote sensing, the time sequence consistency of the tidal flat remote sensing submerged frequency is improved, and the tidal flat terrain inversion model constructed by using data in any period can be applicable to all monitoring time sequences, so that the efficiency is higher.
The application is realized by the following technical scheme:
A tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction comprises the following steps:
collecting time sequence synthetic aperture radar remote sensing images and laser satellite height measurement data according to the research time and a target area;
Processing the synthetic aperture radar remote sensing image to extract tidal flat inundation information, preprocessing laser satellite height measurement data, and obtaining tidal flat priori elevation data;
dividing a time sequence according to an observation period to synthesize an aperture radar remote sensing image, reconstructing a remote sensing image time sequence of each period, and calculating the tidal flat remote sensing inundation frequency of each pixel correction based on the reconstructed remote sensing image time sequence and the extracted tidal flat inundation information to obtain the tidal flat remote sensing inundation frequency corresponding to each period;
and constructing a tidal flat terrain inversion model based on the tidal flat remote sensing inundation frequency corrected in any period and the tidal flat priori elevation data obtained by preprocessing, mapping the tidal flat remote sensing inundation frequency in each period into the tidal flat terrain by using the model, and completing the time sequence monitoring of the tidal flat terrain.
In the above technical scheme, further, the synthetic aperture radar remote sensing image needs to be imaged in a VV polarization mode, the laser satellite height measurement data is ICESat-2 data, and the orbit data needs to be intersected with the target area.
Further, the processing and extracting tidal flat inundation information from the synthetic aperture radar remote sensing image specifically includes:
and (3) screening the time sequence synthetic aperture radar remote sensing image, reserving the remote sensing image capable of being segmented, carrying out speckle noise filtering on each reserved remote sensing image, and segmenting the remote sensing image through a self-adaptive threshold value to realize land and water segmentation to obtain tidal flat inundation information.
Further, the screening time sequence synthetic aperture radar remote sensing image reserves the remote sensing image which can be used for cutting the land and the water, and specifically comprises the following steps: and acquiring a gray level histogram of the synthetic aperture radar remote sensing image, and taking the number of peaks and the distance between the two peaks as screening indexes, wherein only the synthetic aperture radar remote sensing image with two peaks and the distance between the two peaks larger than a preset threshold value in the gray level histogram is reserved.
Further, the preprocessing of the laser satellite height measurement data is performed to obtain the tidal flat priori elevation data, which specifically includes:
the laser satellite height measurement data on the surface of the tidal flat are visually extracted, and the sea water surface height measurement data which is in the tidal flat range and does not have fluctuation characteristics are removed;
And then, using the pixel size of the remote sensing image to provide grids for the laser satellite height measurement data, obtaining the center coordinates of the pixels as the positions of the corresponding grids, and carrying out mean value operation on the laser five-star height measurement data in the same grid as the elevation of the grid to obtain tidal flat priori elevation data.
Further, the tidal flat remote sensing submerged frequency is calculated as follows:
firstly, dividing a time sequence synthetic aperture radar remote sensing image into different monitoring periods according to an observation period;
For the time sequence remote sensing images in each period, dividing the time sequence remote sensing images into different tide level intervals at fixed intervals according to tide levels during imaging of the remote sensing images, and counting remote sensing observation times of the different tide level intervals;
calculating the quantity of remote sensing images to be reserved in the corresponding tide level interval according to the predicted tide level distribution;
removing redundant remote sensing images in each tide level interval;
and finally, calculating the tidal flat remote sensing inundation frequency corrected by each pixel based on the reconstructed remote sensing image time sequence to obtain the tidal flat remote sensing inundation frequency in each period.
Further, referring to the predicted tide level distribution, the number of remote sensing images to be reserved in the corresponding tide level interval is calculated, specifically: selecting a section with the largest difference between the theoretical probability and the actual probability of each section and the actual probability not being 0, calculating the total number of the target images according to the theoretical probability and the actual image number of the section, namely dividing the actual image number of the section by the theoretical probability of the section, and multiplying the total number of the target images by the theoretical probability of each section to obtain the number of the remote sensing images which should be reserved in each section.
Further, when redundant remote sensing images are removed, the remote sensing images with smaller distance between two peaks in the gray level histogram corresponding to the remote sensing images are removed preferentially.
Further, the tidal flat remote sensing inundation frequency F of each pixel correction is calculated, and the following formula is adopted:
，
wherein, Representing the number of times the pixel is observed to be submerged in water during the corresponding period, i.e. the number of times it is classified as a body of water in the remote sensing image,The effective observation total number of the calculated pixels in the observation period is represented (considering that the remote sensing image is influenced by cloud rain, and part of pixels in the image are invalid observations).
Further, a tidal flat topography inversion model is constructed by using a machine learning method based on tidal flat remote sensing inundation frequency corrected in any period and tidal flat priori elevation data obtained by preprocessing.
A tidal flat terrain timing monitoring system based on tidal flat remote sensing flooding frequency correction, comprising:
The preprocessing module is used for processing the synthetic aperture radar remote sensing image to extract tidal flat inundation information, preprocessing the laser satellite height measurement data and obtaining tidal flat priori elevation data;
The flood frequency calculation module is used for dividing the time sequence into the aperture radar remote sensing images according to the observation period, reconstructing the remote sensing image time sequence of each period, and calculating the tidal flat remote sensing flood frequency corrected by each pixel based on the reconstructed remote sensing image time sequence and the extracted tidal flat flood information to obtain the tidal flat remote sensing flood frequency corresponding to each period;
The tidal flat terrain inversion module is used for constructing a tidal flat terrain inversion model based on tidal flat remote sensing inundation frequency corrected in any period and tidal flat priori elevation data obtained through pretreatment, mapping the tidal flat remote sensing inundation frequency in each period into the tidal flat terrain by utilizing the model, and completing time sequence monitoring of the tidal flat terrain.
Aiming at the requirements of the fields of tidal flat terrain monitoring, resource management and the like, the application provides a tidal flat terrain time sequence monitoring method and system based on tidal flat remote sensing inundation frequency correction. Based on a time sequence Synthetic Aperture Radar (SAR) remote sensing image, the predicted tide level data is used as an auxiliary reconstruction image time sequence, so that tide level distribution of the remote sensing images in different periods tends to be consistent, tidal flat remote sensing submerged frequency correction is realized, the consistency of the tidal flat remote sensing submerged frequencies in different time sequences is improved, and the constructed tidal flat topography inversion model can be applied to different periods to invert time sequence tidal flat topography, thereby realizing the time sequence observation of the tidal flat topography. The method aims to solve the limitations and inefficiency of the traditional tidal flat terrain remote sensing detection method in time sequence monitoring, provides reliable technical support for development and protection of tidal flat resources, and has great practical value. The application provides a high-efficiency and convenient mode for monitoring the tidal flat topography time sequence, is an innovation in the application of the remote sensing information technology, and is a beneficial supplement of the tidal flat topography remote sensing detection method.
Drawings
FIG. 1 is a flow chart of a tidal flat terrain timing monitoring method based on tidal flat remote sensing inundation frequency correction;
FIG. 2 is a graph of the result of time-series tidal flat inundation frequency correction in a head of bay area;
FIG. 3 is a plot of results of a time-series tidal flat topography inversion for a head of bay area;
FIG. 4 is a plot of accuracy evaluation scatter of the results of a time-series tidal flat terrain inversion for a head of bay area;
fig. 5 is a block diagram of a tidal flat terrain timing monitoring system based on tidal flat remote sensing flooding frequency correction.
Detailed Description
The technical scheme of the application is further described in detail below with reference to the attached drawings and specific embodiments. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the application, whereby the application is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction of the application comprises the following steps:
(1) Collecting time sequence synthetic aperture radar remote sensing images and laser satellite height measurement data according to the research time and a target area; the synthetic aperture radar remote sensing image needs to be imaged in a VV polarization mode; the laser satellite data refers to Ice, cloud, and Land Elevation Satellite-2 (ICESat-2) data, whose orbit data must intersect with the target tidal flat area.
(2) Filtering the sequential synthetic aperture radar remote sensing image, reserving the remote sensing image capable of being segmented on land and water, carrying out speckle noise filtering on each reserved remote sensing image, segmenting the remote sensing image through a self-adaptive threshold value, and extracting tidal flat inundation information; the screening of the remote sensing images is performed by two indexes of the number of peaks and the distance between the two peaks, and for a gray histogram of the synthetic aperture radar remote sensing images, the synthetic aperture radar remote sensing images with the gray histogram having the two peaks and the distance between the two peaks being larger than a preset threshold value, wherein the preset threshold value can be set according to experience. For each of the retained synthetic aperture radar remote sensing images, pixels are aggregated into objects to smooth speckle noise in the synthetic aperture radar remote sensing images using a Simple Non-ITERATIVE CLUSTERING (SNIC) algorithm, which can be referenced （Achanta R, Susstrunk S. Superpixels and Polygons Using Simple Non-iterative Clustering; proceedings of the IEEE Conf Comput Vis Pattern Recognit, Honolulu, HI, USA, F July 21-26 2017）. and then an Otsu algorithm is used to generate an adaptive segmentation threshold to amphibious segment the image to extract tidal flat inundation information, which can be referenced （Otsu N. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 1979, 9(1): 62-6.）.
(3) Preprocessing laser satellite height measurement data to obtain tidal flat surface height measurement data, namely tidal flat priori elevation; the preprocessing mainly comprises the steps of carrying out photon extraction on the tidal flat and matching the geographic position, specifically, extracting laser altimetry data of the surface of the tidal flat by visual observation, removing the seawater surface altimetry data which are in the tidal flat range but have no obvious fluctuation characteristics, providing grids for the laser altimetry data by using the pixel size of the remote sensing image, obtaining the pixel center coordinates as the positions of the grids, carrying out mean value operation on the laser altimetry data falling in the same grids, and obtaining the priori elevation of the tidal flat as the elevation of the grids.
(4) Dividing the time sequence image according to the observation period, reconstructing the image time sequence of each period, and calculating the corrected tidal flat remote sensing submerged frequency based on the reconstructed image time sequence to obtain the tidal flat remote sensing submerged frequency of each period; specific: firstly, dividing the time sequence images into different monitoring periods according to the observation period, and counting the remote sensing observation times of different intervals according to the tide level when the remote sensing images are imaged for the time sequence images in each period at fixed intervals. Then, based on the forecast tide level distribution, the quantity of the remote sensing images which should be reserved in the corresponding interval is calculated, and redundant remote sensing images in each interval are further removed; and finally, calculating corrected tidal flat remote sensing inundation frequency based on the reconstructed remote sensing image time sequence to obtain the tidal flat remote sensing inundation frequency in each period.
(5) And constructing a tidal flat terrain inversion model based on the corrected tidal flat remote sensing inundation frequency and the preprocessed satellite height measurement data, and constructing the model by a machine learning method.
(6) And mapping the tidal flat remote sensing inundation frequency of each period into the tidal flat topography by using a tidal flat topography inversion model, and completing the time sequence monitoring of the tidal flat topography.
The technical difficulty of tidal flat topography time sequence monitoring based on submerged frequency is that: how to effectively eliminate the influence of the remote sensing observation quantity and the difference of the tide level distribution on the tidal flat inundation frequency of different time sequences so as to have obvious deviation. According to the application, the predicted water level distribution histogram is used as a reference, and the remote sensing image is screened based on the water level during remote sensing, so that the water level distribution of the reconstructed remote sensing image is as close as possible to the predicted water level distribution. The method utilizes the reconstructed remote sensing image to calculate the corrected tidal flat remote sensing inundation frequency. The method is a key step, can effectively overcome the influence of the remote sensing observation quantity and the difference of the tide level distribution on the tide beach inundation frequency of different time sequences, and improves the time sequence consistency of the tide beach remote sensing inundation frequency.
Example 1
In this embodiment, a certain head of bay area is taken as a target area, and the tidal flat topography time sequence monitoring method based on tidal flat remote sensing submerged frequency correction according to the present application performs an experiment, and the method includes the following steps:
(1) Collecting ground range detection data and laser satellite height measurement data of a time sequence synthetic aperture radar remote sensing image according to the research time and a target area; the example is based on a Google EARTH ENGINE (GEE) platform, and uses Sentinel-1 synthetic aperture radar data as a data source to carry out collection work of the remote sensing images of the synthetic aperture radar. The GEE platform is a geographic data processing platform based on cloud computing, has strong computing capacity, and can be used for online visual processing and analyzing of massive remote sensing images and other earth observation data. The data flag of the Sentinel-1 image ground range detection data in the GEE is named 'COPERNICUS/S1_GRD'. The period of 2017 to 2022 was screened to cover the head of bay region, and all Sentinel-1 images imaged in VV polarization mode had 416 views.
And acquiring ICESat-2 satellite laser altimetry data of the study time period and the study area. This data comes from the website "https: record/nsidc. Org/data/data-access-tool/ATL03/versions/5". All available ICESat-2 data that intersect the head of bay region is screened out.
(2) Time sequence synthetic aperture radar remote sensing image water Liu Fenge. Firstly, acquiring a gray level histogram of the synthetic aperture radar remote sensing image, describing the characteristics of the gray level histogram of the synthetic aperture radar remote sensing image through two indexes such as the number of peaks, the distance between the two peaks and the like, and reserving the synthetic aperture radar remote sensing image with the gray level histogram having the two peaks and the enough distance between the two peaks. For each of the retained synthetic aperture radar remote sensing images, pixels are aggregated into objects using a Simple Non-ITERATIVE CLUSTERING (SNIC) algorithm to smooth out speckle noise in the synthetic aperture radar remote sensing images. An Otsu algorithm is then used to generate an adaptive segmentation threshold for amphibious segmentation of the image to extract tidal flat inundation information.
(3) And preprocessing the laser satellite height measurement data. The method comprises the steps of firstly, visually extracting laser height measurement data of the surface of the tidal flat, and removing the height measurement data of the sea water surface which is in the tidal flat range but does not have the fluctuation characteristic. And then, using the pixel size of the remote sensing image to provide a grid for the laser altimetry data, obtaining a central coordinate as the position of the grid, and carrying out mean value operation on the laser altimetry data falling in the same grid as the elevation of the grid to obtain the tidal flat priori elevation.
(4) Tidal flat remote sensing flooding frequency correction. The specific method is that firstly, the time sequence images are divided into different monitoring periods according to the observation period of 2 years: in 2017-2018, 2019-2020 and 2021-2022, for each time sequence image in each period, counting remote sensing observation times in different intervals according to the tide level when the remote sensing image is imaged at fixed intervals. And then, referring to the predicted tide level distribution, calculating the number of the remote sensing images which should be reserved in the corresponding interval. 136 images can be used after the images which cannot be segmented are removed in 2019-2020, the maximum value of the sea level of a research area is 4m, the minimum value of the sea level of the research area is-4 m, 20 intervals are uniformly divided according to the sea level, and the sea level range of the first interval is [ -4m, -3.6m ]. According to the predicted tide level distribution, the theoretical probability of the images in each section (equal to the probability of the dense tide level in the section) is [0.00431,0.0104,0.0256,0.0360,0.0517,0.0692,0.0765,0.0781,0.0739,0.0662,0.0669,0.0705,0.0857,0.0886,0.0739,0.0614,0.0388,0.0149,0.0071,0.0002], in sequence, but due to the existence of sampling errors, the actual probability of the images in each section is [0.0074,0.0370,0.0519,0.0519,0.0519,0.1407,0.1185,0.1111,0.0667,0.0444,0.0370,0.0444,0.0519,0.0815,0.0667,0.0370,0,0,0,0], in sequence, so that the difference between the theoretical probability and the actual probability is the largest (the largest difference is selected to ensure that the number of the images to be reserved is smaller than or equal to the number of the actual images in the section) and the actual probability is not 0, namely the 12 th section. And calculating the total number of target images according to the theoretical probability and the actual image number of the interval, wherein the total number of images to be reserved is equal to the actual image number of the interval divided by the theoretical probability of the interval, the actual image number of the 12 th interval is 6, the theoretical probability is 0.0705, the total number of target images is 85, and the total number of target images is multiplied by the theoretical probability of each interval to obtain the number of remote sensing images to be reserved of each interval. And (3) removing redundant remote sensing images in each interval based on the calculated result, and referring to the step (2) when removing redundant images, preferentially removing images with small distance between two peaks of the gray level histogram of the remote sensing images so as to ensure that the remote sensing images with better land and water segmentation conditions are reserved. And finally, calculating corrected tidal flat remote sensing submerged frequency based on the reconstructed remote sensing image time sequence, wherein a calculation formula is as follows:
，
wherein, Representing the number of observations made by the pixel that are flooded (classified as water in the remote sensing image),Representing the total number of active observations of the picture elements over an observation period. The tidal flat remote sensing inundation frequency of 2017-2018, 2019-2020 and 2021-2022 is obtained (figure 2).
(5) And (5) constructing a tidal flat terrain inversion model. Using the tidal flat remote sensing inundation frequency in 2019-2020 and the laser altimetry data in 2019-2020 as training and verification data (according to 7:3), and constructing a tidal flat terrain inversion model through support vector regression.
(6) And (5) time sequence tidal flat topography inversion and accuracy verification. The tide beach remote sensing inundation frequency in 2017-2018, 2019-2020 and 2021-2022 is mapped into the tide beach topography by using a tide beach topography inversion model, tide beach topography data (figure 3) in 2017-2018, 2019-2020 and 2021-2022 are obtained, and tide beach topography time sequence monitoring is completed. Further using measured water depth data acquired in 2019, laser altimetry data in 2021 to 2022 and laser altimetry data in 2019 to 2020 which do not participate in training to carry out accuracy verification on time sequence tidal flat terrain inversion. The verification result shows that based on laser altimetry data verification, the Root Mean Square Error (RMSE) of tidal flat topography in 2019-2020 and 2021-2022 is respectively 0.34 m and 0.45 m, and the determination coefficient (R 2) is respectively 0.83 and 0.76; the tidal flat topography RMSE was 0.43 m and r 2 was 0.76 as shown in fig. 4, which was verified using the measured data from 2019-2020.
Through the technical process, the tide beach topography time sequence monitoring of the target area is finally successfully completed. The data type of the result is raster data, the raster attribute value represents the tidal flat elevation, and the pixel size is the same as the image resolution.
Example 2
In this embodiment, as shown in fig. 5, there is provided a tidal flat terrain timing monitoring system based on tidal flat remote sensing flooding frequency correction, including:
The preprocessing module is used for processing the synthetic aperture radar remote sensing image to extract tidal flat inundation information, preprocessing the laser satellite height measurement data and obtaining tidal flat priori elevation data;
The flood frequency calculation module is used for dividing the time sequence into the aperture radar remote sensing images according to the observation period, reconstructing the remote sensing image time sequence of each period, and calculating the tidal flat remote sensing flood frequency corrected by each pixel based on the reconstructed remote sensing image time sequence and the extracted tidal flat flood information to obtain the tidal flat remote sensing flood frequency corresponding to each period;
The tidal flat terrain inversion module is used for constructing a tidal flat terrain inversion model based on tidal flat remote sensing inundation frequency corrected in any period and tidal flat priori elevation data obtained through pretreatment, mapping the tidal flat remote sensing inundation frequency in each period into the tidal flat terrain by utilizing the model, and completing time sequence monitoring of the tidal flat terrain.
The system is used to implement the method of tidal beach topography timing monitoring based on tidal beach remote sensing inundation frequency correction referred to in the present application, furthermore, it should be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (10)
1. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction is characterized by comprising the following steps of:
collecting time sequence synthetic aperture radar remote sensing images and laser satellite height measurement data according to the research time and a target area;
Processing the synthetic aperture radar remote sensing image to extract tidal flat inundation information, preprocessing laser satellite height measurement data, and obtaining tidal flat priori elevation data;
dividing a time sequence according to an observation period to synthesize an aperture radar remote sensing image, reconstructing a remote sensing image time sequence of each period, and calculating the tidal flat remote sensing inundation frequency of each pixel correction based on the reconstructed remote sensing image time sequence and the extracted tidal flat inundation information to obtain the tidal flat remote sensing inundation frequency corresponding to each period;
and constructing a tidal flat terrain inversion model based on the tidal flat remote sensing inundation frequency corrected in any period and the tidal flat priori elevation data obtained by preprocessing, mapping the tidal flat remote sensing inundation frequency in each period into the tidal flat terrain by using the model, and completing the time sequence monitoring of the tidal flat terrain.
2. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 1, wherein the synthetic aperture radar remote sensing image is required to be imaged in a VV polarization mode, the laser satellite height measurement data is ICESat-2 data, and orbit data of the laser satellite height measurement data is required to intersect with a target area.
3. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 1, wherein the processing of the synthetic aperture radar remote sensing image extracts tidal flat inundation information, specifically comprises:
and (3) screening the time sequence synthetic aperture radar remote sensing image, reserving the remote sensing image capable of being segmented, carrying out speckle noise filtering on each reserved remote sensing image, and segmenting the remote sensing image through a self-adaptive threshold value to realize land and water segmentation to obtain tidal flat inundation information.
4. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 3, wherein the screening time sequence is synthesized by an aperture radar remote sensing image, and the remote sensing image capable of dividing land and water is reserved, specifically: and acquiring a gray level histogram of the synthetic aperture radar remote sensing image, and taking the number of peaks and the distance between the two peaks as screening indexes, wherein only the synthetic aperture radar remote sensing image with two peaks and the distance between the two peaks larger than a preset threshold value in the gray level histogram is reserved.
5. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 1, wherein the preprocessing of the laser satellite height measurement data is performed to obtain tidal flat priori elevation data, specifically:
the laser satellite height measurement data on the surface of the tidal flat are visually extracted, and the sea water surface height measurement data which is in the tidal flat range and does not have fluctuation characteristics are removed;
And then, using the pixel size of the remote sensing image to provide grids for the laser satellite height measurement data, obtaining the center coordinates of the pixels as the positions of the corresponding grids, and carrying out mean value operation on the laser five-star height measurement data in the same grid as the elevation of the grid to obtain tidal flat priori elevation data.
6. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 1, wherein the tidal flat remote sensing inundation frequency is calculated as follows:
firstly, dividing a time sequence synthetic aperture radar remote sensing image into different monitoring periods according to an observation period;
For the time sequence remote sensing images in each period, dividing the time sequence remote sensing images into different tide level intervals at fixed intervals according to tide levels during imaging of the remote sensing images, and counting remote sensing observation times of the different tide level intervals;
calculating the quantity of remote sensing images to be reserved in the corresponding tide level interval according to the predicted tide level distribution;
removing redundant remote sensing images in each tide level interval;
and finally, calculating the tidal flat remote sensing inundation frequency corrected by each pixel based on the reconstructed remote sensing image time sequence to obtain the tidal flat remote sensing inundation frequency in each period.
7. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 6, wherein when redundant remote sensing images are removed, the remote sensing images with smaller distance between two peaks in a corresponding gray level histogram of the remote sensing images are removed preferentially.
8. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 6, wherein the tidal flat remote sensing inundation frequency F corrected by each pixel is calculated by adopting the following formula:
，
wherein, Representing the number of times the pixel is observed to be submerged in water during the corresponding period, i.e. the number of times it is classified as a body of water in the remote sensing image,Representing the total number of valid observations of the calculated picture elements over the observation period.
9. The tidal flat topography time sequence monitoring method based on tidal flat remote sensing inundation frequency correction according to claim 1, wherein a machine learning method is used for constructing a tidal flat topography inversion model based on tidal flat remote sensing inundation frequency corrected in any period and tidal flat priori elevation data obtained through pretreatment.
10. Tidal flat topography time sequence monitoring system based on tidal flat remote sensing inundation frequency correction, which is characterized by comprising:
The preprocessing module is used for processing the synthetic aperture radar remote sensing image to extract tidal flat inundation information, preprocessing the laser satellite height measurement data and obtaining tidal flat priori elevation data;
The flood frequency calculation module is used for dividing the time sequence into the aperture radar remote sensing images according to the observation period, reconstructing the remote sensing image time sequence of each period, and calculating the tidal flat remote sensing flood frequency corrected by each pixel based on the reconstructed remote sensing image time sequence and the extracted tidal flat flood information to obtain the tidal flat remote sensing flood frequency corresponding to each period;
The tidal flat terrain inversion module is used for constructing a tidal flat terrain inversion model based on tidal flat remote sensing inundation frequency corrected in any period and tidal flat priori elevation data obtained through pretreatment, mapping the tidal flat remote sensing inundation frequency in each period into the tidal flat terrain by utilizing the model, and completing time sequence monitoring of the tidal flat terrain.
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