CN116310499A - Ship yaw detection method for optical remote sensing image - Google Patents
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Abstract
The invention discloses a ship yaw detection method of an optical remote sensing image, which relates to the technical field of remote sensing and comprises the following specific steps: s1: collecting remote sensing image data; s2: processing image data; s3: planning and importing a route; s4: detecting a ship; s5: yaw early warning; s6: and correcting the route. According to the ship yaw detection method of the optical remote sensing image, firstly, scanning images of a plurality of spectral bands are obtained through multispectral scanning identification, remote sensing image ground object extraction is carried out through data correction and image classification, then data information is completely led into detection equipment through data conversion, data storage and database establishment, route planning is achieved through combining with image data of a navigation area, route leading-in is completed, in the navigation process, remote sensing image data comparison is carried out according to ship positioning information to achieve yaw prediction, early warning is timely carried out when a ship has yaw tendency, and correction is carried out on an actual route of the ship through a deviation correcting route.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a ship yaw detection method of an optical remote sensing image.
Background
The optical remote sensing technology is a technology for remotely acquiring target and environment information in an ultraviolet-infrared optical band. Optical remote sensing systems are typically composed of remote sensors, remote sensing platforms, information transmission and information processing equipment, and the like. As an important earth observation technology, the optical remote sensing image has the characteristics of hyperspectrum, high space and high time resolution, and is mainly used in the fields of reconnaissance, monitoring, missile early warning, weather forecast and the like.
The invention of application number CN202110677403.X discloses an automatic ship matching method based on optical satellite remote sensing images; the automatic ship matching method based on the optical satellite remote sensing image, similar to the method applied above, has the following defects: although the automatic matching can be carried out on the ship, the detection matching can only be carried out on the ship, when the ship driving route gradually deviates, the early warning and intelligent intervention cannot be carried out, so that the effect of correcting the route of the ship cannot be achieved, and the detected ship has the risk of driving out of a navigation area.
Accordingly, in view of the above, research and improvement are made on the existing structure and the existing defects, and a ship yaw detection method for optical remote sensing images is proposed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a ship yaw detection method of an optical remote sensing image, which solves the problems in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme, and the ship yaw detection method of the optical remote sensing image comprises the following specific steps:
s1: remote sensing image data acquisition
S11: multispectral scanning: simultaneously recording and transmitting information (pixels) of spectrum reflection energy of a plurality of to tens of wavebands on a certain scanned point by utilizing a light splitting and photoelectric technology, and forming a frame of scanning image by using the scanning pixels of the same waveband to obtain scanning images of a plurality of spectrum wavebands;
s12: data correction: performing geometric correction on the remote sensing image, accurately matching and superposing the images with geometric precision, the map or the same ground object elements in the data set, which are acquired by different sensors, with each other, adopting the images as a basic data set, and correcting other images by taking a scene image as a basis;
s13: remote sensing image classification: dividing the corrected image by utilizing an image dividing technology, and dividing the obtained homogeneous image object (image spot) according to the minimum unit of image classification so as to realize higher-level remote sensing image classification and target ground object extraction;
s2: image data processing:
s21: data conversion: the image scanner is utilized to scan the image spots of the remote sensing image, and digital data is obtained in a scanning mode, so that the image data are converted into digital data, and analog/digital (A/D) conversion is realized;
s22: and (3) data storage: transferring the digital data of the remote sensing image to a general carrier such as CCT which can be read by a general digital computer and detection equipment;
s23: database establishment: and integrating and summarizing the remote sensing image digital data stored in the detection equipment by using a computer system to form a remote sensing image database, and continuously perfecting and updating the data in the database.
S3: route planning and importing:
s31: planning a navigation route: the method comprises the steps of calling river remote sensing image data in a database to obtain relevant image data of a navigation area, and analyzing and calculating the image data by a computer system according to a relevant algorithm to plan all feasible navigation routes;
s32: route data import: importing all route data of navigation routes of the navigation area into a system of the ship detection equipment;
s4: and (3) ship detection:
s41: determining a planned route: inputting a navigation starting point and a navigation end point into ship detection equipment before navigation, and after the optimal route analysis and feasibility analysis of the system, the detection equipment sequentially provides all feasible navigation routes according to an optimal route recommendation sequence, manually selecting and determining one navigation route, and determining the navigation route as a planned route;
s42: positioning a ship: the ship runs along the river course according to the planned route, in the course of sailing, the ship is positioned in real time through the cloud satellite, and meanwhile, the remote sensing image acquisition equipment acquires real-time remote sensing image data of the ship and surrounding areas in real time according to positioning information;
s43: the image data comparison computer system processes the real-time remote sensing image data, simultaneously retrieves the remote sensing image data in the database, matches the remote sensing image data with the real-time remote sensing image data, and realizes remote sensing image data comparison by using a computer algorithm, thereby obtaining the information of the detected ship such as size, azimuth, sailing direction and the like;
s5: yaw early warning:
s51: yaw prediction: the detection equipment pre-judges the actual sailing route according to the collected relevant information of the sailing ship, and pre-judges whether the ship has yaw tendency or not through the comparison of the actual sailing route and the planned sailing route;
s52: early warning broadcasting: for ships with yaw tendency, the detection equipment pre-reports the yaw condition through the voice broadcasting system, and intelligent intervention is performed before the ship yaw, so that the ship is prevented from thoroughly yawing and exiting out of a navigation area;
s6: route correction:
s61: planning a deviation rectifying route: after the detection device feeds the predicted yaw information back to the ship, the deviation degree of the ship and the planned route can be calculated and analyzed through an internal algorithm of the system, and a deviation correcting route returning to the planned route is planned;
s62: returning to planned airlines: and feeding the deviation correcting route back to the ship user through the voice broadcasting system, correcting the actual route of the ship, and prompting the ship to return to the planned route again.
Further, the geometric correction in S12 includes orthographic correction, RPC model correction and polynomial model correction, where the orthographic correction is to select some ground control points on the photo, resample the image into an orthographic image by using DEM data of the original photo and inclination and projection difference values of the corrected image, splice and inlay the several orthographic images, and cut out the image in a certain range after color balance processing.
Furthermore, the remote sensing image processing is performed by replacing a complex strict physical correction model with the RPC model.
Furthermore, the polynomial model correction is used for expressing and calculating the coordinate relation between corresponding points of the images before and after correction by using a polynomial, and is mainly applied to the situation that the ground object is relatively flat.
Further, the segmentation of the image segmentation technique in S13 is based on spectral information of the feature itself and spatial information of the feature, and the spatial information includes elements such as shape, texture, area, and size.
Further, the comparing the remote sensing image data in S42 includes the following steps: and rasterizing the remote sensing image data and the real-time remote sensing image data in the database obtained by matching respectively, and obtaining difference information of the two groups of image data by comparing the differences of the two groups of grid units and combining the resolution ratio of the image data.
Further, the voice broadcasting system in S52 may convert the text information formed by the detecting device into voice information.
The invention provides a ship yaw detection method of an optical remote sensing image, which has the following beneficial effects: according to the ship yaw detection method of the optical remote sensing image, firstly, scanning images of a plurality of spectral bands are obtained through multispectral scanning identification, remote sensing image ground object extraction is carried out through data correction and image classification, then data information is completely led into detection equipment through data conversion, data storage and database establishment, route planning is achieved through combining with image data of a navigation area, route leading-in is completed, in the navigation process, remote sensing image data comparison is carried out according to ship positioning information to achieve yaw prediction, early warning is timely carried out when a ship has yaw tendency, and correction is carried out on an actual route of the ship through a deviation correcting route.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical solutions of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments.
A ship yaw detection method of an optical remote sensing image comprises the following specific steps:
s1: remote sensing image data acquisition
S11: multispectral scanning: simultaneously recording and transmitting information (pixels) of spectrum reflection energy of a plurality of to tens of wavebands on a certain scanned point by utilizing a light splitting and photoelectric technology, and forming a frame of scanning image by using the scanning pixels of the same waveband to obtain scanning images of a plurality of spectrum wavebands;
s12: data correction: performing geometric correction on the remote sensing image, accurately matching and superposing the images with geometric precision, the map or the same ground object elements in the data set, which are acquired by different sensors, with each other, adopting the images as a basic data set, and correcting other images by taking a scene image as a basis;
s13: remote sensing image classification: dividing the corrected image by utilizing an image dividing technology, and dividing the obtained homogeneous image object (image spot) according to the minimum unit of image classification so as to realize higher-level remote sensing image classification and target ground object extraction;
s2: image data processing:
s21: data conversion: the image scanner is utilized to scan the image spots of the remote sensing image, and digital data is obtained in a scanning mode, so that the image data are converted into digital data, and analog/digital (A/D) conversion is realized;
s22: and (3) data storage: transferring the digital data of the remote sensing image to a general carrier such as CCT which can be read by a general digital computer and detection equipment;
s23: database establishment: and integrating and summarizing the remote sensing image digital data stored in the detection equipment by using a computer system to form a remote sensing image database, and continuously perfecting and updating the data in the database.
S3: route planning and importing:
s31: planning a navigation route: the method comprises the steps of calling river remote sensing image data in a database to obtain relevant image data of a navigation area, and analyzing and calculating the image data by a computer system according to a relevant algorithm to plan all feasible navigation routes;
s32: route data import: importing all route data of navigation routes of the navigation area into a system of the ship detection equipment;
s4: and (3) ship detection:
s41: determining a planned route: inputting a navigation starting point and a navigation end point into ship detection equipment before navigation, and after the optimal route analysis and feasibility analysis of the system, the detection equipment sequentially provides all feasible navigation routes according to an optimal route recommendation sequence, manually selecting and determining one navigation route, and determining the navigation route as a planned route;
s42: positioning a ship: the ship runs along the river course according to the planned route, in the course of sailing, the ship is positioned in real time through the cloud satellite, and meanwhile, the remote sensing image acquisition equipment acquires real-time remote sensing image data of the ship and surrounding areas in real time according to positioning information;
s43: the image data comparison computer system processes the real-time remote sensing image data, simultaneously retrieves the remote sensing image data in the database, matches the remote sensing image data with the real-time remote sensing image data, and realizes remote sensing image data comparison by using a computer algorithm, thereby obtaining the information of the detected ship such as size, azimuth, sailing direction and the like;
s5: yaw early warning:
s51: yaw prediction: the detection equipment pre-judges the actual sailing route according to the collected relevant information of the sailing ship, and pre-judges whether the ship has yaw tendency or not through the comparison of the actual sailing route and the planned sailing route;
s52: early warning broadcasting: for ships with yaw tendency, the detection equipment pre-reports the yaw condition through the voice broadcasting system, and intelligent intervention is performed before the ship yaw, so that the ship is prevented from thoroughly yawing and exiting out of a navigation area;
s6: route correction:
s61: planning a deviation rectifying route: after the detection device feeds the predicted yaw information back to the ship, the deviation degree of the ship and the planned route can be calculated and analyzed through an internal algorithm of the system, and a deviation correcting route returning to the planned route is planned;
s62: returning to planned airlines: and feeding the deviation correcting route back to the ship user through the voice broadcasting system, correcting the actual route of the ship, and prompting the ship to return to the planned route again.
The geometric correction in S12 comprises orthographic correction, RPC model correction and polynomial model correction, wherein the orthographic correction is to select some ground control points on a photo, resample the image into an orthographic image by using DEM data of the original photo and inclination and projection difference values of the corrected image, splice and inlay a plurality of orthographic images, and cut out images in a certain range after color balance treatment.
The RPC model correction is to replace a complex strict physical correction model by the RPC model for remote sensing image processing.
The polynomial model correction is to express and calculate the coordinate relation between corresponding points of the images before and after correction by using a polynomial, and is mainly applied to the condition that the ground object is relatively flat.
The segmentation of the image segmentation technique in S13 is based on spectral information of the feature itself and spatial information of the feature, and the spatial information includes elements such as shape, texture, area, and size.
The remote sensing image data comparison in S42 includes the following steps: and rasterizing the remote sensing image data and the real-time remote sensing image data in the database obtained by matching respectively, and obtaining difference information of the two groups of image data by comparing the differences of the two groups of grid units and combining the resolution ratio of the image data.
The voice broadcasting system in S52 may convert the text information formed by the detecting device into voice information.
The ship yaw detection method of the optical remote sensing image comprises the following specific steps of:
s1: remote sensing image data acquisition
S11: multispectral scanning: simultaneously recording and transmitting information (pixels) of spectrum reflection energy of a plurality of to tens of wavebands on a certain scanned point by utilizing a light splitting and photoelectric technology, and forming a frame of scanning image by using the scanning pixels of the same waveband to obtain scanning images of a plurality of spectrum wavebands;
s12: data correction: carrying out geometric correction on a remote sensing image, accurately matching and superposing images with geometric precision, maps or the same ground object elements in the data set, which are acquired by different sensors, adopting the images as a basic data set, taking a scene image as a basis, and correcting other images, wherein the geometric correction comprises orthographic correction, RPC model correction and polynomial model correction, the orthographic correction is carried out by selecting a plurality of ground control points on a photo, resampling the image into an orthographic image by utilizing DEM data of the original photo and inclination and projection difference values of the corrected image, splicing and embedding a plurality of orthographic images, and cutting out the image in a certain range after color balance treatment; the RPC model correction is to replace a complex strict physical correction model by the RPC model for remote sensing image processing; the polynomial model correction is to express and calculate the coordinate relation between corresponding points of the images before and after correction by using a polynomial, and is mainly applied to the condition that the ground object is relatively flat;
s13: remote sensing image classification: dividing the corrected image by utilizing an image dividing technology, and dividing the obtained homogeneous image object (image spot) according to the minimum unit of image classification so as to realize higher-level remote sensing image classification and target ground object extraction, wherein the dividing of the image dividing technology in S13 is based on spectrum information of the ground object and space information of the ground object, and the space information comprises the elements of shape, texture, area, size and the like;
s2: image data processing:
s21: data conversion: the image scanner is utilized to scan the image spots of the remote sensing image, and digital data is obtained in a scanning mode, so that the image data are converted into digital data, and analog/digital (A/D) conversion is realized;
s22: and (3) data storage: transferring the digital data of the remote sensing image to a general carrier such as CCT which can be read by a general digital computer and detection equipment;
s23: database establishment: and integrating and summarizing the remote sensing image digital data stored in the detection equipment by using a computer system to form a remote sensing image database, and continuously perfecting and updating the data in the database.
S3: route planning and importing:
s31: planning a navigation route: the method comprises the steps of calling river remote sensing image data in a database to obtain relevant image data of a navigation area, and analyzing and calculating the image data by a computer system according to a relevant algorithm to plan all feasible navigation routes;
s32: route data import: importing all route data of navigation routes of the navigation area into a system of the ship detection equipment;
s4: and (3) ship detection:
s41: determining a planned route: inputting a navigation starting point and a navigation end point into ship detection equipment before navigation, and after the optimal route analysis and feasibility analysis of the system, the detection equipment sequentially provides all feasible navigation routes according to an optimal route recommendation sequence, manually selecting and determining one navigation route, and determining the navigation route as a planned route;
s42: positioning a ship: the ship runs along the river course according to the planned route, in the course of sailing, the ship is positioned in real time through the cloud satellite, and meanwhile, the remote sensing image acquisition equipment acquires real-time remote sensing image data of the ship and surrounding areas in real time according to positioning information;
s43: the image data comparison computer system processes the real-time remote sensing image data, simultaneously retrieves the remote sensing image data in the database, matches the remote sensing image data with the real-time remote sensing image data, and realizes remote sensing image data comparison by using a computer algorithm so as to obtain the information of the detected ship such as size, azimuth, sailing direction and the like, wherein the remote sensing image data comparison in S42 comprises the following steps: respectively rasterizing remote sensing image data and real-time remote sensing image data in the database obtained by matching, and obtaining difference information of two groups of image data by comparing the differences of two groups of grid units and combining the resolution of the image data;
s5: yaw early warning:
s51: yaw prediction: the detection equipment pre-judges the actual sailing route according to the collected relevant information of the sailing ship, and pre-judges whether the ship has yaw tendency or not through the comparison of the actual sailing route and the planned sailing route;
s52: early warning broadcasting: for the ship with yaw tendency, the detection equipment pre-reports the yaw condition through the voice broadcasting system, intelligent intervention is carried out before the ship yaw, so that the ship is prevented from thoroughly yawing and driving out of a navigation area, wherein the voice broadcasting system in S52 can convert text information formed by the detection equipment into voice information;
s6: route correction:
s61: planning a deviation rectifying route: after the detection device feeds the predicted yaw information back to the ship, the deviation degree of the ship and the planned route can be calculated and analyzed through an internal algorithm of the system, and a deviation correcting route returning to the planned route is planned;
s62: returning to planned airlines: and feeding the deviation correcting route back to the ship user through the voice broadcasting system, correcting the actual route of the ship, and prompting the ship to return to the planned route again.
According to the ship yaw detection method of the optical remote sensing image, firstly, scanning images of a plurality of spectral bands are obtained through multispectral scanning identification, remote sensing image ground object extraction is carried out through data correction and image classification, then data information is completely led into detection equipment through data conversion, data storage and database establishment, route planning is achieved through combining with image data of a navigation area, route leading-in is completed, in the navigation process, remote sensing image data comparison is carried out according to ship positioning information to achieve yaw prediction, early warning is timely carried out when a ship has yaw tendency, and correction is carried out on an actual route of the ship through a deviation correcting route.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (7)
1. The ship yaw detection method of the optical remote sensing image is characterized by comprising the following specific steps of:
s1: remote sensing image data acquisition
S11: multispectral scanning: simultaneously recording and transmitting information (pixels) of spectrum reflection energy of a plurality of to tens of wavebands on a certain scanned point by utilizing a light splitting and photoelectric technology, and forming a frame of scanning image by using the scanning pixels of the same waveband to obtain scanning images of a plurality of spectrum wavebands;
s12: data correction: performing geometric correction on the remote sensing image, accurately matching and superposing the images with geometric precision, the map or the same ground object elements in the data set, which are acquired by different sensors, with each other, adopting the images as a basic data set, and correcting other images by taking a scene image as a basis;
s13: remote sensing image classification: dividing the corrected image by utilizing an image dividing technology, and dividing the obtained homogeneous image object (image spot) according to the minimum unit of image classification so as to realize higher-level remote sensing image classification and target ground object extraction;
s2: image data processing:
s21: data conversion: the image scanner is utilized to scan the image spots of the remote sensing image, and digital data is obtained in a scanning mode, so that the image data are converted into digital data, and analog/digital (A/D) conversion is realized;
s22: and (3) data storage: transferring the digital data of the remote sensing image to a general carrier such as CCT which can be read by a general digital computer and detection equipment;
s23: database establishment: and integrating and summarizing the remote sensing image digital data stored in the detection equipment by using a computer system to form a remote sensing image database, and continuously perfecting and updating the data in the database.
S3: route planning and importing:
s31: planning a navigation route: the method comprises the steps of calling river remote sensing image data in a database to obtain relevant image data of a navigation area, and analyzing and calculating the image data by a computer system according to a relevant algorithm to plan all feasible navigation routes;
s32: route data import: importing all route data of navigation routes of the navigation area into a system of the ship detection equipment;
s4: and (3) ship detection:
s41: determining a planned route: inputting a navigation starting point and a navigation end point into ship detection equipment before navigation, and after the optimal route analysis and feasibility analysis of the system, the detection equipment sequentially provides all feasible navigation routes according to an optimal route recommendation sequence, manually selecting and determining one navigation route, and determining the navigation route as a planned route;
s42: positioning a ship: the ship runs along the river course according to the planned route, in the course of sailing, the ship is positioned in real time through the cloud satellite, and meanwhile, the remote sensing image acquisition equipment acquires real-time remote sensing image data of the ship and surrounding areas in real time according to positioning information;
s43: the image data comparison computer system processes the real-time remote sensing image data, simultaneously retrieves the remote sensing image data in the database, matches the remote sensing image data with the real-time remote sensing image data, and realizes remote sensing image data comparison by using a computer algorithm, thereby obtaining the information of the detected ship such as size, azimuth, sailing direction and the like;
s5: yaw early warning:
s51: yaw prediction: the detection equipment pre-judges the actual sailing route according to the collected relevant information of the sailing ship, and pre-judges whether the ship has yaw tendency or not through the comparison of the actual sailing route and the planned sailing route;
s52: early warning broadcasting: for ships with yaw tendency, the detection equipment pre-reports the yaw condition through the voice broadcasting system, and intelligent intervention is performed before the ship yaw, so that the ship is prevented from thoroughly yawing and exiting out of a navigation area;
s6: route correction:
s61: planning a deviation rectifying route: after the detection device feeds the predicted yaw information back to the ship, the deviation degree of the ship and the planned route can be calculated and analyzed through an internal algorithm of the system, and a deviation correcting route returning to the planned route is planned;
s62: returning to planned airlines: and feeding the deviation correcting route back to the ship user through the voice broadcasting system, correcting the actual route of the ship, and prompting the ship to return to the planned route again.
2. The method for detecting the yaw of a ship by using an optical remote sensing image according to claim 1, wherein the geometric correction in S12 includes orthographic correction, RPC model correction and polynomial model correction, and the orthographic correction is implemented by selecting some ground control points on a photo, resampling the image into an orthographic image by using DEM data of the original photo and inclination and projection differences of the corrected image, splicing and embedding a plurality of orthographic images, and clipping the image within a certain range after color balance processing.
3. The method for detecting the yaw of the ship by using the optical remote sensing image according to claim 2, wherein the correction of the RPC model is to replace a complex strict physical correction model by the RPC model for remote sensing image processing.
4. The method for detecting the yaw of the ship by using the optical remote sensing image according to claim 2, wherein the polynomial model correction is used for expressing and calculating the coordinate relation between corresponding points of the image before and after correction by using a polynomial, and is mainly applied to the condition that the ground object is relatively flat.
5. The method for detecting the yaw of a ship by using an optical remote sensing image according to claim 1, wherein the segmentation of the image segmentation technique in S13 is based on spectral information of the ground object itself and spatial information of the ground object, and the spatial information includes elements such as shape, texture, area, and size.
6. The method for detecting the yaw of a ship from an optical remote sensing image according to claim 1, wherein the comparing of the remote sensing image data in S42 comprises the steps of: and rasterizing the remote sensing image data and the real-time remote sensing image data in the database obtained by matching respectively, and obtaining difference information of the two groups of image data by comparing the differences of the two groups of grid units and combining the resolution ratio of the image data.
7. The method for detecting the yaw of a ship by using an optical remote sensing image according to claim 1, wherein the voice broadcasting system in S52 can convert text information formed by the detecting device into voice information.
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