CN113744249B - Marine ecological environment damage investigation method - Google Patents
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Abstract
The invention discloses a marine ecological environment damage investigation method, which comprises the steps of constructing a marine ecological environment damage remote sensing investigation information base according to different marine ecological environment damage types; according to the constructed marine ecological environment damage remote sensing investigation information base, determining an optimal remote sensing observation mode corresponding to a marine ecological environment damage event to be investigated; acquiring remote sensing image data corresponding to the marine ecological environment damage event to be investigated according to the determined optimal remote sensing observation mode; performing image preprocessing on the acquired remote sensing image data; and carrying out marine ecological environment damage feature recognition and extraction on the preprocessed remote sensing image to generate a remote sensing investigation result of a marine ecological environment damage event. According to the invention, the marine ecological environment damage is investigated by utilizing an automatic screening remote sensing means, so that artificial subjective uncertainty in remote sensing satellite data selection and processing method selection is avoided, and the efficiency and accuracy of marine ecological environment damage investigation are improved.
Description
Technical Field
The invention relates to the technical field of marine ecological environment investigation, in particular to a marine ecological environment damage investigation method.
Background
With the rapid development of economy, the ocean is increasingly more pronounced in the economic development. In the ocean development and utilization process, on one hand, the demand of human beings for resources is increasingly increased along with the development of socioeconomic performance; on the other hand, the unreasonable exploitation and utilization of environmental resources by human beings or accidents cause ecological environmental damage. The development disturbance, pollution emission or unexpected accidents can generate serious damage to the marine ecology, and the damage to the marine ecology environment has become a great obstacle for realizing the harmony between people and natural and the sustainable development of the ocean.
The marine ecological environment damage has the characteristics of multiple pollution sources, strong persistence, wide diffusion range, difficult prevention and control, large harm and the like. Conventional sea surface monitoring means include a coastal sea automatic monitoring station (a practical system suitable for regional sea environment prediction service, but requiring periodic cleaning or recalibration), a seabed-based or underwater automatic monitoring system (for early investigation, engineering operation and environment guarantee of a marine engineering environment), a buoy system (for fixed-point, continuous, long-term monitoring of hydrologic, meteorological and water quality parameters of a sea surface, subsurface or deep sea, the data can also be used as ground truth calibration for satellite remote sensing sea application), a ship-based automatic monitoring system (a plurality of investigation monitoring tasks can be performed in one navigation, but manual participation in monitoring and improvement of real-time data processing and real-time analysis capability are required), a high-frequency ground wave radar sea surface environment control technology (remote detection, using a sky wave detection distance of about 800-3000km, mainly used for military purposes, sea wave and ocean current prediction can be improved), and the monitoring means have high cost, limited area and high personnel and equipment coverage area dependence. The remote sensing technology, especially the satellite remote sensing technology, has the advantages of large spatial scale and dynamic observation, is not limited by geographic position, accessibility, sea investigation and the like, and therefore, the remote sensing technology has remarkable advantages in aspects of sea surface environment damage time point judgment, pollutant type and range identification, pollutant space-time migration dynamic investigation and the like.
From the category of marine environmental damage, it is largely divided into engineering activities (dredging, sea-filling, cross-sea construction, etc.); production and living sewage (industrial, agricultural, domestic wastewater and waste discharge); accident type pollution (oil spill, hazardous chemical substances, solid waste, etc.), etc. The marine ecological environment damage types are different, the damage types have different forms, duration and space scales, and the spectrum characteristics, the remote sensing visibility and the data archival rate are different greatly, so that the suitability for remote sensing and the required methods are greatly different, and a targeted remote sensing investigation method is required to be adopted respectively.
The existing marine ecological environment damage remote sensing investigation method is not mature, is generally limited to a single satellite platform or sensor type, is not directed against remote sensing data and data processing methods required by automatic matching of characteristics of different damage, has high artificial subjective dependence on selection of remote sensing satellite data selection and processing methods, and has the defects of difficult data and method selection, insufficient applicability and nonstandard technical flow in the rapid application process of marine ecological environment damage investigation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a marine ecological environment damage investigation method, which aims to solve the problems of strong subjective dependence, low processing efficiency and incapability of quickly and dynamically matching data and algorithms in the existing marine ecological environment damage investigation method.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
a marine environmental damage investigation method, comprising the steps of:
s1, constructing a marine ecological environment damage remote sensing investigation information base according to different marine ecological environment damage types;
s2, determining an optimal remote sensing observation mode corresponding to the marine ecological environment damage event to be investigated according to the constructed marine ecological environment damage remote sensing investigation information base;
s3, acquiring remote sensing image data corresponding to the marine ecological environment damage event to be investigated according to the determined optimal remote sensing observation mode;
s4, performing image preprocessing on the acquired remote sensing image data;
s5, carrying out marine ecological environment damage feature recognition and extraction on the preprocessed remote sensing image, and generating a remote sensing investigation result of a marine ecological environment damage event.
Further, in the step S1, the marine ecological environment damage remote sensing investigation information base specifically includes:
sensitive waves Duan Ku corresponding to different marine ecological environment damage types;
a corresponding relation table between sensitive wave bands corresponding to different marine ecological environment damage types and satellite sensors containing the sensitive wave bands;
a time resolution and spatial resolution attribute table of the satellite sensor;
And the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution.
Further, the step S1 specifically includes the following sub-steps:
s1-1, constructing sensitive waves Duan Ku corresponding to different marine ecological environment damage types according to the different marine ecological environment damage types;
s1-2, screening all remote sensing satellite sensors according to sensitive wave bands corresponding to different marine ecological environment damage types based on a satellite sensor information table corresponding to the sensitive wave bands, and establishing a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands;
s1-3, establishing a time resolution and spatial resolution attribute table of the satellite sensor;
s1-4, establishing a corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution.
Further, the step S2 specifically includes the following sub-steps:
s2-1, screening satellite sensors containing sensitive wave bands from a sensitive wave band library and a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands, wherein the sensitive wave band library and the corresponding relation table are constructed in the step S1 and correspond to different marine ecological environment damage types according to damage types of marine ecological environment damage events to be investigated;
S2-2, selecting a remote sensing observation platform meeting a revisit period from a time resolution attribute table of the satellite sensor established in the step S1 according to the duration of the marine ecological environment damage event to be investigated;
s2-3, determining an observation period for acquiring remote sensing data according to the damage type and occurrence time point of the marine ecological environment damage event to be investigated and the time resolution of the satellite sensor;
s2-4, selecting the corresponding required remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and the required remote sensing image spatial resolution established in the step S1 according to the spatial position and the preliminary range of the marine ecological environment damage event to be investigated, superposing the determined satellite sensor, spatial resolution and observation period conditions, and determining the optional remote sensing sensor and observation platform.
Further, the step S2-1 specifically comprises the following sub-steps:
s2-1-1, searching corresponding sensitive wave bands or sensitive wave band combinations in sensitive waves Duan Ku corresponding to different marine ecological environment damage types constructed in the step S1 according to the damage types of marine ecological environment damage events to be investigated;
S2-1-2, according to the searched sensitive wave band or sensitive wave band combination, the satellite sensor containing the sensitive wave band is selected from a corresponding relation table between the sensitive wave band established in the step S1 and the satellite sensor containing the sensitive wave band.
Further, the step S2-2 specifically comprises the following sub-steps:
s2-2-1, determining an observation period according to the duration of occurrence, development, migration and diffusion processes of the marine ecological environment damage event to be investigated;
s2-2-2, selecting a remote sensing satellite or a remote sensing observation platform with the observation spatial resolution meeting the requirement of the observation period from the time resolution attribute table of the satellite sensor established in the step S1 according to the determined observation period.
Further, the step S2-3 specifically comprises the following sub-steps:
s2-3-1, calculating the initial observation date and the final observation date of the required remote sensing observation according to the damage type of the marine ecological environment damage event to be investigated, the estimated occurrence time point and the estimated duration, and the reserved time quantity before and after;
the calculation mode of the initial observation date is as follows:
T s ＝T 0 -Δt 1
wherein T is s To initiate the observation date, T 0 For the moment of occurrence of damage Δt 1 Reserving a quantity for an observation time before occurrence of the damage;
The calculation mode of the termination observation date is as follows:
T e ＝T 0 +Δt 2
wherein T is e To terminate the observation date, Δt 2 For the duration of the observation time after the occurrence of the lesion;
s2-3-2, taking the period from the start observation date to the end observation date as the finally determined remote sensing observation period [ T ] s ～T e ]。
Further, the step S2-4 specifically comprises the following sub-steps:
s2-4-1, setting a buffer area in an outward expansion mode by taking the space position of a marine ecological environment damage event to be investigated as the center according to wind speed, ocean current and diffusion properties, selecting an undisturbed area outside the buffer area as a baseline comparison area, and determining a target area of remote sensing investigation according to the sum of the ranges;
s2-4-2, selecting corresponding required corresponding remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution established in the step S1 according to the determined size of the remote sensing investigation target area, and determining an optional remote sensing sensor and an observation platform according to the selected spatial resolution.
Further, the step S4 specifically includes the following sub-steps:
s4-1, judging the data type of the acquired remote sensing image data; if the image is the optical remote sensing image, executing the step S4-2; if the SAR data is the SAR data, executing the step S4-3;
S4-2, performing image preprocessing on the acquired optical remote sensing image, and specifically comprising the following sub-steps:
s4-2-1, firstly, performing geometric rough correction on an optical remote sensing image according to the spatial position change relation of deformed pixel points in the optical remote sensing image, then, transforming coordinate values of pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the optical remote sensing image by utilizing various correction functions;
s4-2-2, firstly, selecting a plurality of optical remote sensing images acquired under different sensors or different conditions in the same area, taking one optical remote sensing image with known coordinate information in the same area as a reference image to select a control point, then selecting the same object on the reference image and an image to be registered in the area, establishing a coordinate conversion relation between the reference image and other images to be registered based on coordinate pairs of a plurality of homonymy points on different images, and finally registering the object images by utilizing the homonymy points of the reference image;
s4-2-3, firstly establishing a quantitative relation between a digital quantized value and a radiation brightness value in a view field corresponding to the digital quantized value for radiation calibration, then converting the radiation brightness value of an atmospheric top layer into a solar radiation brightness value reflected by the earth surface, carrying out atmosphere correction by adopting an absolute atmosphere correction method based on a radiation transmission model, and finally adjusting the average brightness of an image for solar altitude angle correction;
S4-2-4, splicing a plurality of adjacent optical remote sensing images in an area according to geographic positions to generate a complete optical remote sensing image;
s4-2-5, performing color balance processing on the generated synthetic optical remote sensing image, and adjusting the colors of the images in different time phases to be consistent;
s4-3, performing image preprocessing on SAR data of the synthetic aperture radar, and specifically comprising the following sub-steps:
s4-3-1, firstly, performing geometric rough correction on a remote sensing image according to a spatial position change relation of deformed pixel points of the remote sensing image in SAR data, then, transforming pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the remote sensing image by utilizing various correction functions;
s4-3-2, firstly, selecting a plurality of remote sensing images acquired under different sensors or different conditions in the same area, taking a remote sensing image with known coordinate information in the same area as a reference image to select a control point, then selecting the same object on the reference image and an image to be registered in the area, establishing a coordinate conversion relation between the reference image and other images to be registered based on coordinate pairs of a plurality of homonymous points on different images, and finally registering the object images by utilizing the homonymous points of the reference image;
S4-3-3, firstly calculating the mean value and variance of the local remote sensing image, then carrying out self-adaptive filtering calculation according to the mean value and variance of samples in a filtering window, and carrying out linear combination on the observed intensity and the local average intensity in a fixed window by taking a minimum mean square error criterion as an objective function to construct an optimized linear filter; finally, filtering and moving the pixels on the image one by one to finish the traversal of all the pixels;
s4-3-4, dividing the image into a plurality of strips, adjusting each pixel by row, calculating the average value of each strip and the average value of the whole image, and then calculating the adjusted pixel value of each pixel, wherein the calculation formula is as follows:
wherein f (i) m ，j m ) Mean for the original image pixel value of the ith row and jth column in the mth stripe m Is the Mean value of the mth band, mean is the Mean value of the entire image,and adjusting the pixel value for each pixel point.
Further, the step S5 specifically includes the following sub-steps:
s5-1, calculating a gray level co-occurrence matrix of two pixel combinations in the image, dividing the remote sensing image, and extracting a damage area;
s5-2, projecting the vector boundary of the extracted damage region to an equal-area rectangular coordinate system for projection conversion, and calculating the area of the damage region;
S5-3, combining a plurality of images of different time phases in the investigation period, verifying the extraction result of the damaged area by using a longitudinal time sequence change detection method, and performing superposition calculation on the damaged area and the change of the area;
s5-4, taking an unaffected area outside the damage range as a space contrast area, extracting the spectrum characteristics of a damage baseline, combining a plurality of images of different phases in the investigation period, and checking the change of the baseline area along with time by using a longitudinal time sequence change detection method as a reference for determining the time and degree of damage of the marine ecological environment;
s5-5, generating remote sensing investigation results of marine ecological environment damage events according to quantitative information of time, range and degree of marine ecological environment damage.
The invention has the following beneficial effects:
according to the method, an observation platform, a sensor, a spectrum band and a space-time resolution database are constructed according to the forms, duration and space scales of different damage types, remote sensing images meeting the conditions are automatically screened according to damage characteristics, and image filtering, image segmentation, feature extraction and damage area statistics methods are established; the remote sensing means of automatic screening is utilized to investigate the marine ecological environment damage, so that artificial subjective uncertainty in remote sensing satellite data selection and processing method selection is avoided, and the efficiency and accuracy of marine ecological environment damage investigation are improved.
Drawings
FIG. 1 is a schematic flow chart of a marine ecological environment damage investigation method in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the embodiment of the invention provides a marine ecological environment damage investigation method, which comprises the following steps S1 to S5:
s1, constructing a marine ecological environment damage remote sensing investigation information base according to different marine ecological environment damage types;
in this embodiment, the marine ecological environment damage remote sensing investigation information base specifically includes:
sensitive waves Duan Ku corresponding to different marine ecological environment damage types;
a corresponding relation table between sensitive wave bands corresponding to different marine ecological environment damage types and satellite sensors containing the sensitive wave bands;
A time resolution and spatial resolution attribute table of the satellite sensor;
and the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution.
The step S1 specifically comprises the following sub-steps:
s1-1, constructing sensitive waves Duan Ku corresponding to different marine ecological environment damage types according to the different marine ecological environment damage types;
specifically, the method constructs a sensitive wave band library corresponding to each damage type by selecting common marine ecological environment damage types, such as a visible light wave band corresponding to sea engineering and solid waste, a red wave band and a red edge wave band corresponding to sea surface plants, wherein the microwave wave band is preferred for oil spilling, and the ultraviolet and visible light wave bands are selected.
S1-2, screening all remote sensing satellite sensors according to sensitive wave bands corresponding to different marine ecological environment damage types based on a satellite sensor information table corresponding to the sensitive wave bands, and establishing a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands;
s1-3, establishing a time resolution and spatial resolution attribute table of the satellite sensor;
s1-4, establishing a corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution; the relationship between lesion area and desired spatial resolution of the remote sensing image is expressed as:
Wherein R is the required remote sensing image spatial resolution, S min For the area of the damaged area, K is a resolution conversion coefficient, also called Kell coefficient.
S2, determining an optimal remote sensing observation mode corresponding to the marine ecological environment damage event to be investigated according to the constructed marine ecological environment damage remote sensing investigation information base;
in this embodiment, the step S2 specifically includes the following sub-steps:
s2-1, according to damage types of marine ecological environment damage events to be investigated, screening satellite sensors containing sensitive wave bands from a sensitive wave band library and a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands, wherein the sensitive wave band library and the corresponding relation table are constructed in the step S1 and correspond to different marine ecological environment damage types, and the satellite sensors comprise the following steps:
s2-1-1, searching corresponding sensitive wave bands or sensitive wave band combinations in sensitive waves Duan Ku corresponding to different marine ecological environment damage types constructed in the step S1 according to the damage types of marine ecological environment damage events to be investigated;
s2-1-2, screening a satellite sensor containing a sensitive wave band from a corresponding relation table between the sensitive wave band established in the step S1 and the satellite sensor containing the sensitive wave band according to the searched sensitive wave band or sensitive wave band combination;
Specifically, according to the damage types of the marine ecological environment damage event to be investigated, corresponding sensitive wave bands or sensitive wave band combinations are searched and determined in sensitive waves Duan Ku corresponding to different marine ecological environment damage types constructed in the step S1; and then, according to the determined sensitive wave band or sensitive wave band combination, the satellite sensor containing the sensitive wave band is selected from a corresponding relation table between the sensitive wave band constructed in the step S1 and the satellite sensor containing the sensitive wave band.
S2-2, selecting a remote sensing observation platform meeting a revisit period from a time resolution attribute table of the satellite sensor established in the step S1 according to the duration of the marine ecological environment damage event to be investigated, wherein the remote sensing observation platform specifically comprises the following sub-steps:
s2-2-1, determining an observation period according to the duration of occurrence, development, migration and diffusion processes of the marine ecological environment damage event to be investigated;
s2-2-2, selecting a remote sensing satellite or a remote sensing observation platform with the observation spatial resolution meeting the requirement of the observation period from the time resolution attribute table of the satellite sensor established in the step S1 according to the determined observation period;
specifically, according to the duration of occurrence, development, migration, diffusion and the like of the damage event of the marine ecological environment to be investigated, an observation period is determined, the observation period is related to the duration of the damage event, the development process of the damage event can be embodied, the day or the day is generally taken as the period, the season or the year is taken as the period at the longest, and the sensor transit observation is needed in the observation period; and selecting a remote sensing satellite or a remote sensing observation platform, such as a polar orbit satellite, a geosynchronous satellite, unmanned aerial vehicle remote sensing and the like, with the observation spatial resolution meeting the requirement of the observation period from the spatial resolution attribute table of the satellite sensor established in the step S1 according to the determined observation period.
S2-3, determining an observation period for acquiring remote sensing data according to the damage type and occurrence time point of a marine ecological environment damage event to be investigated and combining the time resolution of a satellite sensor, wherein the observation period comprises the following steps:
s2-3-1, calculating the initial observation date and the final observation date of required remote sensing observation according to the damage type of the marine ecological environment damage event to be investigated, the estimated occurrence time point and the estimated duration, and the reserved time before and after the occurrence of the damage, wherein the remote sensing observation time period completely covers the occurrence time period of the damage, and the reserved time before and after the occurrence of the damage;
the calculation mode of the initial observation date is as follows:
T s ＝T 0 -Δt 1
wherein T is s To initiate the observation date, T 0 For the moment of occurrence of damage Δt 1 The observation time reservation before damage occurs can be set to 1-2 remote sensing revisit periods;
the calculation mode of the termination observation date is as follows:
T e ＝T 0 +Δt 2
wherein T is e To terminate the observation date, Δt 2 For the duration of the observation time after the damage occurs, the remote sensing revisit period can be set to be 1-2;
s2-3-2, determining the finally selected remote sensing observation period as [ T ] by evaluating the adjacent periods before, during and after the occurrence of the damage s ～T e ]；
S2-4, selecting the corresponding required remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and the required remote sensing image spatial resolution established in the step S1 according to the spatial position and the preliminary range of the occurrence of the marine ecological environment damage event to be investigated, superposing the determined satellite sensor, spatial resolution and observation period conditions, and determining an optional remote sensing sensor and an observation platform, wherein the method specifically comprises the following steps:
s2-4-1, setting a buffer area in an outward expansion mode by taking the space position of a marine ecological environment damage event to be investigated as the center according to wind speed, ocean current and diffusion properties, selecting an undisturbed area outside the buffer area as a baseline comparison area, and determining a target area of remote sensing investigation according to the sum of the ranges;
s2-4-2, selecting corresponding required corresponding remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution established in the step S1 according to the determined size of the remote sensing investigation target area, and determining an optional remote sensing sensor and an observation platform according to the selected spatial resolution.
S3, acquiring remote sensing image data corresponding to the marine ecological environment damage event to be investigated according to the determined optimal remote sensing observation mode;
in the embodiment, according to the marine ecological environment damage event to be investigated, the steps S1 to S2 are combined, the corresponding time period, range, observation platform, sensor and wave band are determined, and the required remote sensing observation mode is comprehensively screened out; and then preparing corresponding remote sensing data by respective corresponding acquisition modes of different remote sensing data.
S4, performing image preprocessing on the acquired remote sensing image data;
in this embodiment, the step S4 specifically includes the following sub-steps:
s4-1, judging the data type of the acquired remote sensing image data; if the image is the optical remote sensing image, executing the step S4-2; if the SAR data is the SAR data, executing the step S4-3;
s4-2, performing image preprocessing on the acquired optical remote sensing image;
specifically, the invention carries out preprocessing such as geometric correction and registration, radiation correction, splicing and embedding, color adjustment and the like on multispectral satellite remote sensing and unmanned aerial vehicle remote sensing images (optical remote sensing images) to obtain remote sensing images with complete space coverage, low cloud coverage and high quality in a investigation range for subsequent analysis.
The step S4-2 specifically comprises the following sub-steps:
s4-2-1, firstly, performing geometric rough correction on an optical remote sensing image according to the spatial position change relation of deformed pixel points in the optical remote sensing image, then, transforming coordinate values of pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the optical remote sensing image by utilizing various correction functions;
specifically, the invention eliminates or corrects the geometric errors of the remote sensing image through geometric correction. Geometric deformation includes systematic deformation (internal) and random deformation (external). Systematic deformation is caused by a remote sensing system, is deformation which has certain regularity and can be predicted in advance, and can be subjected to system geometric rough correction by adopting a calculation formula and the obtained auxiliary parameters by utilizing a spatial position change relation; the random deformation is random and unpredictable, a mathematical model between pixel coordinates and geographic coordinates of a target object is established by means of control points, and the transformation of pixel positions in different coordinate systems is realized. The control points may be from a given ground control point or selected from an image or vector file with distinct features, known coordinates, etc. For geometric fine correction, a variety of correction functions may be employed, such as: remote sensing image correction based on polynomials, collinearity equations, rational functions, and small-face element differential correction based on automatic registration, etc.
S4-2-2, firstly, selecting a plurality of optical remote sensing images acquired under different sensors or different conditions in the same area, taking one optical remote sensing image with known coordinate information in the same area as a reference image to select a control point, then selecting the same object on the reference image and an image to be registered in the area, establishing a coordinate conversion relation between the reference image and other images to be registered based on coordinate pairs of a plurality of homonymy points on different images, and finally registering the object images by utilizing the homonymy points of the reference image;
specifically, the invention matches and superimposes two or more images acquired by different sensors or under different conditions in the same area to realize image registration. Specifically, an image with known coordinate information in the same area is taken as a reference image, control points are selected, the same target object is selected on the reference image and the image to be registered in the area, and a coordinate conversion relation between the reference image and other images to be registered is established based on coordinate pairs of a plurality of homonymous points on different images, so that registration of the target image by the homonymous points of the reference image is realized. The image registration and the geometric correction principle are the same, and both the spatial position (pixel coordinate) transformation and the pixel gray value resampling process are involved.
S4-2-3, firstly establishing a quantitative relation between a digital quantized value and a radiation brightness value in a view field corresponding to the digital quantized value for radiation calibration, then converting the radiation brightness value of an atmospheric top layer into a solar radiation brightness value reflected by the earth surface, carrying out atmosphere correction by adopting an absolute atmosphere correction method based on a radiation transmission model, and finally adjusting the average brightness of an image for solar altitude angle correction;
specifically, the invention eliminates or reduces image distortion caused by radiation errors through radiation correction, and obtains the solar radiation brightness value or reflectivity of the ground surface true reflection. Including radiometric calibration, atmospheric correction, and terrain and solar altitude correction. The quantitative relation between the digital quantized value and the radiation brightness value in the corresponding field of view is established through radiometric calibration, so that the error generated by the sensor is eliminated. Including relative radiometric calibration and absolute calibration, radiometric calibration can be performed with the aid of the header file and sensor type of the remote sensing image. By means of atmospheric correction, the influence of atmospheric absorption and scattering on radiation transmission is eliminated, and the radiation brightness value (or the reflectivity of the atmospheric top layer) of the atmospheric top layer is converted into the solar radiation brightness value (or the reflectivity of the ground surface) of the ground surface reflection. An absolute atmosphere correction method based on a radiation transmission model, such as MORTAN or a 6S model, is adopted to realize atmosphere correction.
The solar altitude angle correction is due to the phenomenon that radiation difference exists between remote sensing images acquired in different areas and different periods, and the correction can be performed by adjusting the average brightness of the images, and is expressed as follows:
DN′＝DN/sinσ
where DN' is the corrected luminance value, DN is the original luminance value, and σ is the solar altitude.
S4-2-4, splicing a plurality of adjacent optical remote sensing images in an area according to geographic positions to generate a complete optical remote sensing image;
specifically, a plurality of adjacent remote sensing images in an area are spliced into an image with a larger range and no gap between the images through splicing and embedding. And combining the multiple images according to geographic positions through a Seamless Mosaic image Mosaic tool to generate a final composite image.
S4-2-5, performing color balance processing on the generated synthetic optical remote sensing image, and adjusting the colors of the images in different time phases to be consistent;
specifically, when a mosaic is created using a multi-view image, if the color difference between different images is significant, color balance processing is required to adjust the colors of the images in different seasons to be uniform.
S4-3, performing image preprocessing on SAR data of the synthetic aperture radar;
Specifically, the invention performs the processing of geometric correction and registration, image filtering, radiation correction, splicing and embedding and the like on synthetic aperture radar SAR data.
The step S4-3 specifically comprises the following sub-steps:
s4-3-1, firstly, performing geometric rough correction on a remote sensing image according to a spatial position change relation of deformed pixel points of the remote sensing image in SAR data, then, transforming pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the remote sensing image by utilizing various correction functions;
s4-3-2, firstly, selecting control points by taking a plurality of remote sensing images acquired under different sensors or different conditions in the same area and other remote sensing images with known coordinate information in the same area as reference images, then selecting the same target object on the reference images and the images to be registered in the area, establishing a coordinate conversion relation between the reference images and the other images to be registered based on coordinate pairs of a plurality of homonymous points on the different images, and finally registering the target images by utilizing the homonymous points of the reference images;
s4-3-3, firstly calculating the mean value and variance of the local remote sensing image, then carrying out self-adaptive filtering calculation according to the mean value and variance of samples in a filtering window, and carrying out linear combination on the observed intensity and the local average intensity in a fixed window by taking a minimum mean square error criterion as an objective function to construct an optimized linear filter; finally, filtering and moving the pixels on the image one by one to finish the traversal of all the pixels;
Specifically, the invention removes speckle noise in SAR images due to coherent imaging by enhancing Lee filtering. Firstly, calculating local mean and variance, and then, carrying out self-adaptive filtering calculation according to the mean and variance of samples in a filtering window. The observation intensity g and the local average intensity g in the fixed window are used as an objective function by a Minimum Mean Square Error (MMSE) criterion ij And performing linear combination to construct an optimized linear filter. And (3) filtering and moving the pixels on the image one by one, completing the traversal of all the pixels, and realizing the change of local statistics along with the change of the spatial position.
The enhancement Lee filtering adopts three filters according to different image characteristics of different areas:
the first is a uniform region in which the speckle noise can simply be smoothed out with mean filtering;
the second type is an uneven area, texture information is reserved when noise is removed, and Lee filtering is applied;
the third class is the region containing the object of the separation point, the filter should keep the original values as much as possible.
The enhanced Lee filter expression is:
wherein g' ij To enhance the value of the Lee-filtered picture element,is the average value of the filter center pixel (i, j),m and N are pixel rows and columns representing the size of the filtering area; Is the weight function of Lee filtering, +.>C u And C max For threshold value->σ ij Is local standard deviation>L is the imaging vision number.
S4-3-4, dividing the image into a plurality of strips, adjusting each pixel by row, calculating the average value of each strip and the average value of the whole image, and then calculating the adjusted pixel value of each pixel, wherein the calculation formula is as follows:
wherein f (i) m ，j m ) Mean for the original image pixel value of the ith row and jth column in the mth stripe m Is the Mean value of the mth band, mean is the Mean value of the entire image,and adjusting the pixel value for each pixel point.
Specifically, after the image is filtered, the brightness difference from the near-distance point to the far-distance point caused by the side view imaging of the SAR image is eliminated by radiation correction. By adjusting the pixel by pixel according to the row, the image effect is enhanced, and the regional brightness difference in space is eliminated.
S5, carrying out marine ecological environment damage feature recognition and extraction on the preprocessed remote sensing image, and generating a remote sensing investigation result of a marine ecological environment damage event.
In this embodiment, the step S5 specifically includes the following sub-steps:
s5-1, calculating a gray level co-occurrence matrix of two pixel combinations in the image, dividing the remote sensing image, and extracting a damage area;
S5-2, projecting the vector boundary of the extracted damage region to an equal-area rectangular coordinate system for projection conversion, and calculating the area of the damage region;
s5-3, combining a plurality of images of different time phases in the investigation period, verifying the extraction result of the damaged area by using a longitudinal time sequence change detection method, and performing superposition calculation on the damaged area and the change of the area;
s5-4, taking an unaffected area outside the damage range as a space contrast area, extracting the spectrum characteristics of a damage baseline, combining a plurality of images of different phases in the investigation period, and checking the change of the baseline area along with time by using a longitudinal time sequence change detection method as a reference for determining the time and degree of damage of the marine ecological environment;
s5-5, generating remote sensing investigation results of marine ecological environment damage events according to quantitative information of time, range and degree of marine ecological environment damage.
Specifically, the preprocessing result of the image adopts a recognition method aiming at various marine ecological environment damages, and adopts an image segmentation method based on a gray level co-occurrence matrix to carry out image segmentation, so that feature extraction, damage distribution statistics and damage thematic map production are realized.
In order to effectively extract the damaged areas of the marine ecological environment, the damaged areas in the image are segmented by using different texture characteristics in the image and adopting a texture statistical analysis method based on a gray level co-occurrence matrix, and the damaged areas are extracted. The texture statistical analysis method is carried out by adopting a gray level co-occurrence matrix, the gray level space dependence of two pixel combinations in an image is calculated, the occurrence probability of a pair of pixel gray values with the interval distance of d pixels in the direction theta being i and j is calculated, and the definition formula is as follows:
where p (i, j, d, θ) is the number of times a pair of pixels having gray scales i and j appear at a distance d and in a direction θ, respectively. D and theta of optimal texture feature calculation are required to be adjusted along with a specific image, the distance d can be selected to be 3, 5 and 7 which are common, the direction of theta is selected to be four (0 degrees, 45 degrees, 90 degrees and 135 degrees), the gray level co-occurrence matrix is calculated, the image is segmented, and the concerned damage area is extracted.
For the damage types of marine ecological environments with long duration (months, years) such as sea-related projects such as sea reclamation and the like or production and living emission, a plurality of images are required to be respectively acquired in the investigation period determined in the step S4-2, the images are sequentially identified and classified, the damage range is determined, and a time sequence change diagram of the damage range is established.
Converting the raster image or the extracted vector boundary of the damaged area into an equal-area rectangular coordinate system through projection, and counting the area of the damaged area by using Geographic Information System (GIS) software:
Area＝sum(R 2 ×n)
wherein, area is the damage Area, R is the side length (generally square) of a single pixel of the remote sensing image; n is the number of pixels.
And (5) utilizing geographic information system software to manufacture the damage thematic map. Setting paper size, determining a mapping area, adjusting map content, adding a scale, a legend, a compass, setting longitude and latitude grid lines, defining national boundaries and south-ocean islands, and finally outputting a marine ecological environment damage remote sensing investigation result graph meeting the requirements.
The marine ecological environment damage is divided into three types of engineering activities, production living pollution discharge, accident type pollution and the like, an observation platform, a sensor, a spectrum band and a space-time resolution database are constructed according to the forms, duration and space scales of different damage types, remote sensing images meeting the conditions are automatically screened according to damage characteristics, and image filtering, image segmentation, characteristic extraction and damage area statistics methods are established; and the remote sensing means of automatic screening is utilized to investigate the marine ecological environment damage, so that the artificial subjective uncertainty in the selection of the remote sensing satellite data selection and processing method is avoided, and the efficiency and accuracy of the marine ecological environment damage investigation are improved.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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 principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (9)
1. The marine ecological environment damage investigation method is characterized by comprising the following steps:
s1, constructing a marine ecological environment damage remote sensing investigation information base according to different marine ecological environment damage types;
s2, determining an optimal remote sensing observation mode corresponding to the marine ecological environment damage event to be investigated according to the constructed marine ecological environment damage remote sensing investigation information base;
s3, acquiring remote sensing image data corresponding to the marine ecological environment damage event to be investigated according to the determined optimal remote sensing observation mode;
s4, performing image preprocessing on the acquired remote sensing image data; the method specifically comprises the following sub-steps:
s4-1, judging the data type of the acquired remote sensing image data; if the image is the optical remote sensing image, executing the step S4-2; if the SAR data is the SAR data, executing the step S4-3;
s4-2, performing image preprocessing on the acquired optical remote sensing image, and specifically comprising the following sub-steps:
s4-2-1, firstly, performing geometric rough correction on an optical remote sensing image according to the spatial position change relation of deformed pixel points in the optical remote sensing image, then, transforming coordinate values of pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the optical remote sensing image by utilizing various correction functions;
S4-2-2, firstly, selecting a plurality of optical remote sensing images acquired under different sensors or different conditions in the same area, taking one optical remote sensing image with known coordinate information in the same area as a reference image to select a control point, then selecting the same object on the reference image and an image to be registered in the area, establishing a coordinate conversion relation between the reference image and other images to be registered based on coordinate pairs of a plurality of homonymy points on different images, and finally registering the object images by utilizing the homonymy points of the reference image;
s4-2-3, firstly establishing a quantitative relation between a digital quantized value and a radiation brightness value in a view field corresponding to the digital quantized value for radiation calibration, then converting the radiation brightness value of an atmospheric top layer into a solar radiation brightness value reflected by the earth surface, carrying out atmosphere correction by adopting an absolute atmosphere correction method based on a radiation transmission model, and finally adjusting the average brightness of an image for solar altitude angle correction;
s4-2-4, splicing a plurality of adjacent optical remote sensing images in an area according to geographic positions to generate a complete optical remote sensing image;
s4-2-5, performing color balance processing on the generated synthetic optical remote sensing image, and adjusting the colors of the images in different time phases to be consistent;
S4-3, performing image preprocessing on SAR data of the synthetic aperture radar, and specifically comprising the following sub-steps:
s4-3-1, firstly, performing geometric rough correction on a remote sensing image according to a spatial position change relation of deformed pixel points of the remote sensing image in SAR data, then, transforming pixel positions in different coordinate systems by establishing a mathematical model between pixel coordinates and geographic coordinates of a target object, and finally, performing geometric fine correction on the remote sensing image by utilizing various correction functions;
s4-3-2, firstly, selecting a plurality of remote sensing images acquired under different sensors or different conditions in the same area, taking a remote sensing image with known coordinate information in the same area as a reference image to select a control point, then selecting the same object on the reference image and an image to be registered in the area, establishing a coordinate conversion relation between the reference image and other images to be registered based on coordinate pairs of a plurality of homonymous points on different images, and finally registering the object images by utilizing the homonymous points of the reference image;
s4-3-3, firstly calculating the mean value and variance of the local remote sensing image, then carrying out self-adaptive filtering calculation according to the mean value and variance of samples in a filtering window, and carrying out linear combination on the observed intensity and the local average intensity in a fixed window by taking a minimum mean square error criterion as an objective function to construct an optimized linear filter; finally, filtering and moving the pixels on the image one by one to finish the traversal of all the pixels;
S4-3-4, dividing the image into a plurality of strips, adjusting each pixel by row, calculating the average value of each strip and the average value of the whole image, and then calculating the adjusted pixel value of each pixel, wherein the calculation formula is as follows:
wherein f (i) m ,j m ) Mean for the original image pixel value of the ith row and jth column in the mth stripe m Is the Mean value of the mth band, mean is the Mean value of the entire image,the pixel value after the adjustment is used for each pixel point;
s5, carrying out marine ecological environment damage feature recognition and extraction on the preprocessed remote sensing image, and generating a remote sensing investigation result of a marine ecological environment damage event.
2. The marine environmental damage investigation method according to claim 1, wherein in the step S1, the marine environmental damage remote sensing investigation information base specifically comprises:
sensitive waves Duan Ku corresponding to different marine ecological environment damage types;
a corresponding relation table between sensitive wave bands corresponding to different marine ecological environment damage types and satellite sensors containing the sensitive wave bands;
a time resolution and spatial resolution attribute table of the satellite sensor;
and the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution.
3. The marine environmental damage investigation method according to claim 2, wherein the step S1 specifically comprises the following sub-steps:
s1-1, constructing sensitive waves Duan Ku corresponding to different marine ecological environment damage types according to the different marine ecological environment damage types;
s1-2, screening all remote sensing satellite sensors according to sensitive wave bands corresponding to different marine ecological environment damage types based on a satellite sensor information table corresponding to the sensitive wave bands, and establishing a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands;
s1-3, establishing a time resolution and spatial resolution attribute table of the satellite sensor;
s1-4, establishing a corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution.
4. A marine environmental damage investigation method according to claim 3, wherein the step S2 specifically comprises the following sub-steps:
s2-1, screening satellite sensors containing sensitive wave bands from a sensitive wave band library and a corresponding relation table between the sensitive wave bands and the satellite sensors containing the sensitive wave bands, wherein the sensitive wave band library and the corresponding relation table are constructed in the step S1 and correspond to different marine ecological environment damage types according to damage types of marine ecological environment damage events to be investigated;
S2-2, selecting a remote sensing observation platform meeting a revisit period from a time resolution attribute table of the satellite sensor established in the step S1 according to the duration of the marine ecological environment damage event to be investigated;
s2-3, determining an observation period for acquiring remote sensing data according to the damage type and occurrence time point of the marine ecological environment damage event to be investigated and the time resolution of the satellite sensor;
s2-4, selecting the corresponding required remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and the required remote sensing image spatial resolution established in the step S1 according to the spatial position and the preliminary range of the marine ecological environment damage event to be investigated, superposing the determined satellite sensor, spatial resolution and observation period conditions, and determining the optional remote sensing sensor and observation platform.
5. The marine environmental damage investigation method according to claim 4, wherein the step S2-1 specifically comprises the following sub-steps:
s2-1-1, searching corresponding sensitive wave bands or sensitive wave band combinations in sensitive waves Duan Ku corresponding to different marine ecological environment damage types constructed in the step S1 according to the damage types of marine ecological environment damage events to be investigated;
S2-1-2, according to the searched sensitive wave band or sensitive wave band combination, the satellite sensor containing the sensitive wave band is selected from a corresponding relation table between the sensitive wave band established in the step S1 and the satellite sensor containing the sensitive wave band.
6. The marine environmental damage investigation method according to claim 5, wherein the step S2-2 specifically comprises the following sub-steps:
s2-2-1, determining an observation period according to the duration of occurrence, development, migration and diffusion processes of the marine ecological environment damage event to be investigated;
s2-2-2, selecting a remote sensing satellite or a remote sensing observation platform with the observation spatial resolution meeting the requirement of the observation period from the time resolution attribute table of the satellite sensor established in the step S1 according to the determined observation period.
7. The marine environmental damage investigation method according to claim 6, wherein the step S2-3 specifically comprises the following sub-steps:
s2-3-1, calculating the initial observation date and the final observation date of the required remote sensing observation according to the damage type of the marine ecological environment damage event to be investigated, the estimated occurrence time point and the estimated duration, and the reserved time quantity before and after;
The calculation mode of the initial observation date is as follows:
T s ＝T 0 -Δt 1
wherein T is s To initiate the observation date, T 0 For the moment of occurrence of damage Δt 1 Reserving a quantity for an observation time before occurrence of the damage;
the calculation mode of the termination observation date is as follows:
T e ＝T 0 +Δt 2
wherein T is e To terminate the observation date, Δt 2 For the duration of the observation time after the occurrence of the lesion;
s2-3-2, taking the period from the start observation date to the end observation date as the finally determined remote sensing observation period [ T ] s ～T e ]。
8. The marine environmental damage investigation method according to claim 7, wherein the step S2-4 specifically comprises the following sub-steps:
s2-4-1, setting a buffer area in an outward expansion mode by taking the space position of a marine ecological environment damage event to be investigated as the center according to wind speed, ocean current and diffusion properties, selecting an undisturbed area outside the buffer area as a baseline comparison area, and determining a target area of remote sensing investigation according to the sum of the ranges;
s2-4-2, selecting corresponding required corresponding remote sensing image spatial resolution from the corresponding relation table of different marine ecological environment damage types, range sizes and required remote sensing image spatial resolution established in the step S1 according to the determined size of the remote sensing investigation target area, and determining an optional remote sensing sensor and an observation platform according to the selected spatial resolution.
9. The marine environmental damage investigation method according to claim 1, wherein the step S5 specifically comprises the following sub-steps:
s5-1, calculating a gray level co-occurrence matrix of two pixel combinations in the image, dividing the remote sensing image, and extracting a damage area;
s5-2, projecting the vector boundary of the extracted damage region to an equal-area rectangular coordinate system for projection conversion, and calculating the area of the damage region;
s5-3, combining a plurality of images of different time phases in the investigation period, verifying the extraction result of the damaged area by using a longitudinal time sequence change detection method, and performing superposition calculation on the damaged area and the change of the area;
s5-4, taking an unaffected area outside the damage range as a space contrast area, extracting the spectrum characteristics of a damage baseline, combining a plurality of images of different phases in the investigation period, and checking the change of the baseline area along with time by using a longitudinal time sequence change detection method as a reference for determining the time and degree of damage of the marine ecological environment;
s5-5, generating remote sensing investigation results of marine ecological environment damage events according to quantitative information of time, range and degree of marine ecological environment damage.
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