Coastline automatic monitoring method based on high-resolution image

Abstract

The invention discloses a coastline automatic monitoring method based on a high-resolution image, which can accurately acquire and process coastline high-resolution image data shot by a satellite, and eliminates image errors through image preprocessing. And the geometric accuracy and visual effect of the image are ensured. The method comprises the following steps: constructing a coastline feature extraction model, determining the boundary of seawater and land by using spectral analysis and a normalized difference water body index so as to preliminarily extract the position of the coastline, shooting the same area for multiple times through a satellite, obtaining multi-temporal image data of the coastline, further analyzing and calculating the change of the position of the coastline, and determining the position of the coastline. According to the coastline monitoring system, the change quantity of the coastline along with time, the area change and the advancing and retreating distance can be visually displayed, finally, a monitoring result is integrated into a database and visually displayed in a graphical mode through a geographic information system platform, and a scientific basis is provided for management and protection of the coastline.

Description

Technical Field

[0001] The invention relates to the technical field of coastline monitoring, and in particular to an automatic coastline monitoring method based on high-resolution images. Background Art

[0002] The coastline is the boundary between land and sea. The changes in its position and form have a very important impact on the ecological environment, resource development, disaster prevention and control, and economic development of coastal areas. At present, with the impact of global warming and sudden marine disasters such as storm surges, the coastline is constantly eroded by the ocean, resulting in the destruction of coastal wetlands, which is not conducive to the protection of the ecosystem. In addition, it will also have a great impact on coastal ports and affect the land use type and layout of coastal areas. Therefore, it is very necessary to automatically monitor the coastline.

[0003] In the prior art, traditional coastline monitoring methods mainly rely on field measurements and surveys, which not only consumes a lot of manpower, material resources and time, but also has a long monitoring cycle and low efficiency, making it difficult to meet the needs of timely and accurate monitoring of dynamic changes in the coastline. Based on the above reasons, the present invention proposes an automated coastline monitoring method based on high-resolution images to achieve automated monitoring of coastline changes, thereby improving the accuracy and efficiency of coastline monitoring. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention is based on the high-resolution image of the coastline automatic monitoring method can accurately obtain and process the high-resolution image data of the coastline taken by the satellite, eliminate the image error through radiation correction, geometric correction and image enhancement processing, and ensure the geometric accuracy and visual effect of the image. Construct a coastline feature extraction model, use spectral analysis and normalized difference water index to determine the boundary between seawater and land, so as to preliminarily extract the coastline position, and obtain multi-phase image data of the coastline by taking multiple satellite photos of the same area, and further analyze and calculate the change of the coastline position, which can intuitively show the change of the coastline over time, area change and advance and retreat distance, and finally integrate the monitoring results into the database, and intuitively present them in a graphical way through the geographic information system platform, which provides a scientific basis for the management and protection of the coastline; and the present invention proposes a high-resolution image acquisition method based on a specified direction and width, and by continuously scanning satellite images with a specified width on both sides, it can achieve better and more accurate acquisition of the coastline characteristics and positions, so as to facilitate the subsequent automatic and accurate monitoring of the coastline based on the acquired image data.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a coastline automatic monitoring method based on high-resolution images, comprising the following steps:

[0008] S1: Acquisition of high-resolution images of the coastline: Scanning and photographing the coastline area through satellites to obtain high-resolution image data of the coastline with a specified width in a specified direction;

[0009] S2: preprocessing the acquired image data, including radiation correction, geometric correction and image enhancement;

[0010] S3: Construct a coastline feature extraction model based on the pre-processed high-resolution image, complete the coastline feature extraction operation, and determine the coastline position based on the coastline image collected this time;

[0011] S4: acquiring high-resolution images of the coastline again at different times, and extracting coastline features again based on the coastline feature extraction model, and determining the coastline positions of the coastline images at different time nodes;

[0012] S5: Perform comparative analysis based on the extracted coastline location data to achieve automatic monitoring of coastline changes.

[0013] Preferably, the acquisition of high-resolution images of the coastline comprises the following steps:

[0014] S11: Determine the shooting direction of the satellite to the coastline and the width of the image data shot by the satellite in a single shot;

[0015] S12: photographing image data of a coastline on a straight line along a specified direction by a satellite to obtain straight line image data with a specified width;

[0016] S13: After preprocessing the image data and extracting the coastline features, the satellite scanning position is kept unchanged for the image data with coastline features, and the position of the image data without coastline features extracted from the image is extracted from the image data;

[0017] S14: Based on the extracted position data, during the next scanning mission of the satellite, the satellite is used to extend the same width on both sides of the existing width image to acquire images again;

[0018] S15: Repeat the operations in the above steps S13 and S14 until continuous coastline features can be extracted from the image data acquired by the satellite, and the acquisition path of the high-resolution image of the coastline is determined, thereby achieving accurate acquisition of the image data of the coastline.

[0019] Preferably, the radiation correction is used to eliminate radiation errors of the image caused by sensor characteristics, atmospheric scattering and other factors; the geometric correction is to correct the image to a unified geographic coordinate system to ensure the geometric accuracy of the image; the image enhancement is intended to improve the visual effect and readability of the image and highlight the characteristic information of the coastline.

[0020] Preferably, the construction of the coastline feature extraction model based on the pre-processed high-resolution image comprises the following steps:

[0021] S21: Perform spectral analysis on the preprocessed high-resolution images, count the spectral value distribution of seawater and land in each band, and draw a spectral histogram

[0022] S22: Determine the threshold range for distinguishing seawater and land based on the spectral histogram, and use the normalized difference water index to determine the pixels of seawater and land;

[0023] S23: Scan the image pixel by pixel, classify the pixels into seawater pixels and land pixels according to a set threshold, and obtain a binary image, in which the boundary between land and seawater is the initially extracted coastline.

[0024] Preferably, the normalized difference water index calculation formula is: Green represents the green band, NIR represents the near infrared band, and pixels with NDWI less than T2 are set as seawater pixels, and pixels with NDWI greater than T2 are set as land pixels.

[0025] Preferably, the comparative analysis and processing based on the extracted coastline position data includes comparing the coastline position data extracted at different collection times based on the same time node, so as to determine the area change of the coastline over time; comparing the coastline position data extracted at continuous collection times, so as to determine the change of the coastline with the change of tides; comparing the coastline position data extracted at continuous collection times within the same time node, so as to determine the change of the advance and retreat distance of the coastline.

[0026] Preferably, the coastline change calculation uses a distance transformation algorithm to calculate the advance and retreat distance, and uses a polygon area calculation algorithm to obtain the area change. The coastline change map includes a coastline position change map and an area change map.

[0027] Preferably, the coastline monitoring results are output and stored in a database, and are visually displayed on a geographic information system platform.

[0028] (III) Beneficial effects

[0029] Compared with the prior art, the present invention provides an automated coastline monitoring method based on high-resolution images, which has the following beneficial effects: the automated coastline monitoring method based on high-resolution images can accurately acquire and process high-resolution image data of the coastline taken by satellites, eliminate image errors through radiation correction, geometric correction and image enhancement processing, and ensure the geometric accuracy and visual effect of the image. A coastline feature extraction model is constructed, and the boundary between seawater and land is determined by spectral analysis and normalized difference water index, so as to preliminarily extract the coastline position, and the same area is photographed by satellite multiple times to obtain multi-phase image data of the coastline, and the change of the coastline position is further analyzed and calculated, which can intuitively display the change amount, area change and advance and retreat distance of the coastline over time, and finally integrate the monitoring results into the database, and intuitively present them in a graphical manner through the geographic information system platform, which provides a scientific basis for the management and protection of the coastline; and the present invention proposes a high-resolution image acquisition method based on a specified direction and width, and by continuously scanning satellite images with a specified width on both sides, which can achieve better and more accurate acquisition of coastline features and positions, so as to facilitate the subsequent automated and accurate monitoring of the coastline based on the acquired image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure is a flow chart of the automated coastline monitoring method of the present invention. DETAILED DESCRIPTION

[0031] In order to better understand the purpose, structure and function of the present invention, in order to realize automatic and accurate monitoring of the coastline, the present invention is based on high-resolution image automatic monitoring method of coastline, which can accurately obtain and process the high-resolution image data of the coastline taken by satellite, eliminate image errors through radiation correction, geometric correction and image enhancement processing, and ensure the geometric accuracy and visual effect of the image. A coastline feature extraction model is constructed, and the boundary between seawater and land is determined by spectral analysis and normalized difference water index, so as to preliminarily extract the coastline position, and the same area is photographed by satellite multiple times to obtain multi-phase image data of the coastline, and further analyze and calculate the change of the coastline position, which can intuitively show the change amount, area change and advance and retreat distance of the coastline over time, and finally integrate the monitoring results into the database, and intuitively present them in a graphical way through the geographic information system platform, which provides a scientific basis for the management and protection of the coastline; and the present invention proposes a high-resolution image acquisition method based on a specified direction and width, and by continuously scanning satellite images with a specified width on both sides, it can realize better and accurate acquisition of coastline features and positions, so as to facilitate the subsequent automatic and accurate monitoring of the coastline based on the acquired image data. The present invention further describes in detail an automated coastline monitoring method based on high-resolution images.

[0032] refer to Figure 1 The present invention provides a method for automatically monitoring a coastline based on high-resolution images, comprising the following steps:

[0033] S1: Acquisition of high-resolution images of the coastline: Scanning and photographing the coastline area through satellites to obtain high-resolution image data of the coastline with a specified width in a specified direction;

[0034] Specifically, the acquisition of high-resolution images of the coastline includes the following steps:

[0035] S11: Determine the shooting direction of the satellite to the coastline and the width of the image data shot by the satellite in a single shot;

[0036] S12: photographing image data of a coastline on a straight line along a specified direction by a satellite to obtain straight line image data with a specified width;

[0037] S13: After preprocessing the image data and extracting the coastline features, the satellite scanning position is kept unchanged for the image data with coastline features, and the position of the image data without coastline features extracted from the image is extracted from the image data;

[0038] S14: Based on the extracted position data, during the next scanning mission of the satellite, the satellite is used to extend the same width on both sides of the existing width image to acquire images again;

[0039] S15: Repeat the operations in the above steps S13 and S14 until continuous coastline features can be extracted from the image data acquired by the satellite, and the acquisition path of the high-resolution image of the coastline is determined, thereby achieving accurate acquisition of the image data of the coastline.

[0040] Through the above-mentioned high-resolution image acquisition method based on a specified direction and width and by continuously scanning satellite images on both sides with a specified width, better and more accurate acquisition of coastline features and positions can be achieved, thereby facilitating the subsequent automated and accurate monitoring of the coastline based on the acquired image data.

[0041] S2: preprocessing the acquired image data, including radiation correction, geometric correction and image enhancement;

[0042] Specifically, the radiation correction is used to eliminate the radiation error of the image caused by sensor characteristics, atmospheric scattering and other factors; the geometric correction is to correct the image to a unified geographic coordinate system to ensure the geometric accuracy of the image; the image enhancement is intended to improve the visual effect and readability of the image and highlight the characteristic information of the coastline.

[0043] S3: Construct a coastline feature extraction model based on the pre-processed high-resolution image, complete the coastline feature extraction operation, and determine the coastline position based on the coastline image collected this time;

[0044] Specifically, the construction of the coastline feature extraction model based on the pre-processed high-resolution image includes the following steps:

[0045] S21: Perform spectral analysis on the preprocessed high-resolution images, count the spectral value distribution of seawater and land in each band, and draw a spectral histogram

[0046] S22: Determine the threshold range for distinguishing seawater and land based on the spectral histogram, and use the normalized difference water index to determine the pixels of seawater and land;

[0047] Furthermore, the normalized difference water index calculation formula is: Green represents the green band, NIR represents the near infrared band, and pixels with NDWI less than T2 are set as seawater pixels, and pixels with NDWI greater than T2 are set as land pixels.

[0048] S23: Scan the image pixel by pixel, classify the pixels into seawater pixels and land pixels according to a set threshold, and obtain a binary image, in which the boundary between land and seawater is the initially extracted coastline.

[0049] S4: acquiring high-resolution images of the coastline again at different times, and extracting coastline features again based on the coastline feature extraction model, and determining the coastline positions of the coastline images at different time nodes;

[0050] S5: Perform comparative analysis based on the extracted coastline location data to achieve automatic monitoring of coastline changes.

[0051] Specifically, the comparative analysis and processing based on the extracted coastline position data includes comparing the coastline position data extracted at different collection times based on the same time node, so as to determine the area change of the coastline over time; comparing the coastline position data extracted at continuous collection times, so as to determine the change of the coastline with the change of tides; comparing the coastline position data extracted at continuous collection times within the same time node, so as to determine the change of the advance and retreat distance of the coastline. The coastline change calculation adopts a distance transformation algorithm to calculate the advance and retreat distance, and uses a polygonal area calculation algorithm to obtain the area change. The coastline change map includes a coastline position change map and an area change map. By analyzing the coastline, the impact of coastline changes on the ecological environment, resource utilization and other aspects of coastal areas is evaluated, and the future change trend of the coastline is predicted.

[0052] The coastline monitoring results are output and stored in a database, and visualized on a geographic information system platform, which can provide intuitive and convenient coastline information services for relevant departments and personnel.

[0053] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for automated coastline monitoring based on high-resolution images, characterized in that: The steps include: S1: Acquisition of high-resolution images of the coastline: Scanning and photographing the coastline area through satellites to obtain high-resolution image data of the coastline with a specified width in a specified direction; S2: preprocessing the acquired image data, including radiation correction, geometric correction and image enhancement; S3: Construct a coastline feature extraction model based on the pre-processed high-resolution image, complete the coastline feature extraction operation, and determine the coastline position based on the coastline image collected this time; S4: acquiring high-resolution images of the coastline again at different times, and extracting coastline features again based on the coastline feature extraction model, and determining the coastline positions of the coastline images at different time nodes; S5: Perform comparative analysis based on the extracted coastline location data to achieve automatic monitoring of coastline changes.

2. The method for automated coastline monitoring based on high-resolution images according to claim 1, characterized in that: The acquisition of high-resolution images of the coastline includes the following steps: S11: Determine the shooting direction of the satellite to the coastline and the width of the image data shot by the satellite in a single shot; S12: photographing image data of a coastline on a straight line along a specified direction by a satellite to obtain straight line image data with a specified width; S13: After preprocessing the image data and extracting the coastline features, the satellite scanning position is kept unchanged for the image data with coastline features, and the position of the image data without coastline features extracted from the image is extracted from the image data; S14: Based on the extracted position data, during the next scanning mission of the satellite, the satellite is used to extend the same width on both sides of the existing width image to acquire images again; S15: Repeat the operations in the above steps S13 and S14 until continuous coastline features can be extracted from the image data acquired by the satellite, and the acquisition path of the high-resolution image of the coastline is determined, thereby achieving accurate acquisition of the image data of the coastline.

3. The method for automated coastline monitoring based on high-resolution images according to claim 1, characterized in that: The radiation correction is used to eliminate the radiation error of the image caused by sensor characteristics, atmospheric scattering and other factors; the geometric correction is to correct the image to a unified geographic coordinate system to ensure the geometric accuracy of the image; the image enhancement is aimed at improving the visual effect and readability of the image and highlighting the characteristic information of the coastline.

4. The method for automated coastline monitoring based on high-resolution images according to claim 1, characterized in that: The construction of the coastline feature extraction model based on the pre-processed high-resolution image includes the following steps: S21: Perform spectral analysis on the preprocessed high-resolution images, count the spectral value distribution of seawater and land in each band, and draw a spectral histogram S22: Determine the threshold range for distinguishing seawater and land based on the spectral histogram, and use the normalized difference water index to determine the pixels of seawater and land; S23: Scan the image pixel by pixel, classify the pixels into seawater pixels and land pixels according to a set threshold, and obtain a binary image, in which the boundary between land and seawater is the initially extracted coastline.

5. The method for automated coastline monitoring based on high-resolution images according to claim 4, characterized in that: The normalized difference water index calculation formula is: Green represents the green band, NIR represents the near infrared band, and pixels with NDWI less than T2 are set as seawater pixels, and pixels with NDWI greater than T2 are set as land pixels.

6. The method for automated coastline monitoring based on high-resolution images according to claim 1, characterized in that: The comparative analysis based on the extracted coastline position data includes comparing the coastline position data extracted at different acquisition times based on the same time node, so as to determine the area change of the coastline over time; By comparing the coastline position data extracted based on the continuous collection time, it is possible to determine the changes in the coastline with the change of tides; by comparing the coastline position data extracted based on the continuous collection time within the same time node, it is possible to determine the changes in the advance and retreat distance of the coastline.

7. The method for automated coastline monitoring based on high-resolution images according to claim 6, characterized in that: The coastline change calculation adopts a distance transformation algorithm to calculate the advance and retreat distance, and uses a polygon area calculation algorithm to obtain the area change. The coastline change map includes a coastline position change map and an area change map.

8. The method for automated coastline monitoring based on high-resolution images according to claim 1, characterized in that: The coastline monitoring results are output and stored in a database, and are visualized on a geographic information system platform.

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