CN111626260A - Aerial photo ground object feature point extraction method based on unmanned aerial vehicle remote sensing technology - Google Patents
Aerial photo ground object feature point extraction method based on unmanned aerial vehicle remote sensing technology Download PDFInfo
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Abstract
The invention discloses an aerial photograph ground feature point extraction method based on an unmanned aerial vehicle remote sensing technology, which comprises the following steps: acquiring an aerial photo; extracting the central longitude and latitude information of the aerial photo; matching the aerial photo to the position of the longitude and latitude information according to the central longitude and latitude information, the satellite image information and the base map information of the aerial photo; taking the aerial photo after geographic correction as a reference, and identifying the type of the ground object on the satellite image map; and extracting feature points of the ground features from the aerial photo after the ground feature type identification. The invention can more quickly finish the process of extracting the feature points of the ground features, thereby providing an effective method for saving manpower, material resources and financial resources for the future large-scale unmanned aerial vehicle remote sensing processing technology.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to an aerial photograph ground feature point extraction method based on the unmanned aerial vehicle remote sensing technology.
Background
Unmanned aerial vehicle remote sensing, unmanned aerial vehicle and the high-efficient combination of remote sensing technique promptly. Although remote sensing, geographic information systems and global positioning systems (3S technologies) have been developed rapidly in recent years, conventional remote sensing technologies relying only on satellite platforms are greatly limited by objective reasons such as cloud layers and high costs. In addition, the traditional ecological sample frame field investigation method also has the defects that a plurality of research points are difficult to reach and the visual display of investigation data is difficult. And unmanned aerial vehicle remote sensing relies on flexibility, mobility and the convenience of unmanned aerial vehicle system, very big compensaties satellite optical remote sensing easily receives the cloud interference, and high score remote sensing product price and the defect that traditional ecology appearance square frame field investigation method data can not the direct-viewing show, and unmanned aerial vehicle remote sensing can also very easily acquire the regional data that many research personnel did not go to simultaneously.
Although the importance of the unmanned aerial vehicle remote sensing technology is increasingly highlighted, the data volume of the unmanned aerial vehicle remote sensing technology is large or small after the unmanned aerial vehicle aerial photo is obtained. And carry out geographical positioning to large-scale unmanned aerial vehicle remote sensing data, then carry out ground feature point to the aerial photograph after geographical positioning and draw, whole process is extremely loaded down with trivial details, consuming time and wasting force. Although feature points of ground objects of aerial photos are one of the most important basic data of subsequent remote sensing inversion, the existing method does not have an optimal result which particularly saves time.
Disclosure of Invention
The embodiment of the invention provides an aerial photo ground feature point extraction method based on an unmanned aerial vehicle remote sensing technology, which is used for solving the problems in the background technology.
The embodiment of the invention provides an aerial photo ground feature point extraction method based on an unmanned aerial vehicle remote sensing technology, which comprises the following steps: acquiring an aerial photo;
extracting the central longitude and latitude information of the aerial photo;
geographically correcting the aerial photo to the position of the longitude and latitude information according to the central longitude and latitude information and the base map information of the aerial photo;
taking the aerial photo after geographic correction as a reference, and identifying the type of the ground object on the satellite image map;
and extracting feature points of the ground features from the aerial photo after the ground feature type identification.
Further, the method for extracting feature points of the aerial photo ground objects based on the unmanned aerial vehicle remote sensing technology provided by the embodiment of the invention further comprises the following steps:
and naming the aerial photos according to the format of 'track number-photo number-JPG'.
Further, the method for extracting feature points of the aerial photo ground objects based on the unmanned aerial vehicle remote sensing technology provided by the embodiment of the invention further comprises the following steps:
and copying the aerial photos in all tracks to the same folder.
Further, the extracting of the central longitude and latitude information of the aerial photo specifically includes:
determining a field containing the central coordinates of the aerial photograph;
determining geographic coordinates;
and according to the geographic coordinates, extracting the central longitude and latitude information of the aerial photo from a field containing the central coordinates of the aerial photo.
Further, the geographically correcting the aerial photograph to the longitude and latitude information position according to the central longitude and latitude information and the base map information of the aerial photograph specifically includes:
placing the point with the central longitude and latitude information of the aerial photo in the area with the base map information;
placing the aerial photo corresponding to the central longitude and latitude information of the aerial photo in an area with base map information;
and in the same area with the base map information, geographically correcting the aerial photo to the position of the latitude and longitude information.
Further, the identifying the type of the ground object on the satellite image by taking the aerial photograph after the geographic correction as a reference specifically comprises:
establishing different identification points;
assigning values to the identification points according to different ground feature types;
and taking the aerial photo after the geographic correction as a reference, and carrying out ground object type identification and marking on the satellite image.
Further, the method for extracting feature points of the aerial photo ground objects based on the unmanned aerial vehicle remote sensing technology provided by the embodiment of the invention further comprises the following steps:
and automatically and geographically correcting the aerial photo to the position of the latitude and longitude information by setting a correction point in the same area with the base map information.
The embodiment of the invention provides an aerial photo ground feature point extraction method based on an unmanned aerial vehicle remote sensing technology, which has the following beneficial effects compared with the prior art:
according to the method, through aerial photo name modification, aerial photos are unified in a single folder, the latitude and longitude information of the center of the aerial photos is extracted, the aerial photos are matched to the latitude and longitude information positions, the ground feature types are identified and marked, and finally, the time can be saved by more than 70% through the ground feature point extraction after the aerial photos are positioned by the steps. Compared with the conventional visual interpretation and manual operation processes, the method provided by the invention can quickly complete the ground feature point extraction process, and further provides an effective method for saving manpower, material resources and financial resources for the future large-scale unmanned aerial vehicle remote sensing processing technology.
Drawings
Fig. 1 is a schematic flow chart of a method for extracting feature points of a ground object of an aerial photograph based on an unmanned aerial vehicle remote sensing technology according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an aerial photography result of a guizhou province unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 3a is a schematic diagram of an embodiment of an aerial photograph collection folder;
FIG. 3b is an exemplary file for acquiring aerial photographs according to an embodiment of the present invention;
fig. 4 is a modified naming example of aerial photos according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for extracting feature points of a ground object in an aerial photograph based on an unmanned aerial vehicle remote sensing technology, where the method includes:
and step S1, acquiring aerial photos.
And step S2, extracting the central longitude and latitude information of the aerial photo.
And step S3, geographically correcting the aerial photo to the position of the latitude and longitude information according to the latitude and longitude information and the base map information of the center of the aerial photo.
And step S4, taking the aerial photo after geographic correction as a reference, and identifying the land type on the satellite image.
And step S5, performing feature point extraction on the aerial photo after feature type identification.
The specific analysis of the above steps S1 to S5 is as follows:
1. research area overview
The Guizhou karst plateau is at the eastern part of the cloud plateau, is located in the southwest part of China, is a unique unit which is large in altitude gradient, complex in landform condition and extremely fragile in ecological environment and is formed when the Qinghai-Tibet plateau rises, and the relief of topography presents an obvious trend of high east, low west. In the context of a geological environment with widespread carbonate rock and warm humid seasonal wind climates, the area elevation rises from 90m to 2,890m, spans 24 ° N-29 ° N, 103 ° E-109 ° E, and has a total area of 1.76 × 105km 2. The karst landform exposed area accounts for 73%, and belongs to a highland mountain area with the largest area and the strongest development in subtropical cone-shaped karst distribution areas in China and even the world, as shown in fig. 2.
The remote sensing satellite data is Sentinel-2 data, comprises two satellites 2A and 2B, and starts to collect data by using a multispectral imager (MSI) in 2015, 23 months and 2017, 3 months and 07 days respectively. The flying height is 786km, 13 spectral bands can be covered, and the breadth reaches 290 km. The ground resolution is respectively 10m, 20m and 60m, the single revisit period is 10 days, and the revisit period after two stars are complemented is 5 days. The satellite data comprises visible light, near infrared and short wave infrared data, and has three wave band data in the red edge range, and the advantages are obvious in monitoring the vegetation.
Unmanned aerial vehicle data is obtained by the Xinjiang eidolon series product, and unmanned aerial vehicle carries on 1200 ten thousand pixels's sensor, and ground resolution is different because of the flying height difference, from millimeter level to decimeter level.
The unmanned aerial vehicle aerial photo collection uses FragMAP software independently developed by an author team, and the aerial photo processing uses Pycharm and ArcGIS software.
2. Research method
The method mainly relates to 5 parts of working contents, firstly, the file name collected by FragMAP software is modified, then, the modified aerial photos are placed in a unified folder, thirdly, the central longitude and latitude information of all the aerial photos is extracted, fourthly, the aerial photos are matched to the extracted longitude and latitude information, and finally, the ground feature information is identified and marked.
(1) Name modification of aerial photo
After collection using the FragMAP software, the aerial photographs were located under a folder named "flight". After each flight path setting of the FragMAP software, the newly acquired aerial photographs are placed in folders named "1, 2, …" in the diagram of fig. 3a, where "1, 2, …" is the flight path number. The aerial photos of one track are recorded in 1 folder, and the names of the photos are DJI _0001.JPG, DJI _0002.JPG and …' in the picture of FIG. 3 b. Because the aerial photos have larger data volume, and the newly generated aerial photos of the unmanned aerial vehicle can be named after DJI _0001.JPG, DJI _0002.JPG and … by formatting after finishing data each time. Therefore, to distinguish each aerial photograph, a name modification operation is performed on it, which is implemented in PyCharm using the following code.
The modified aerial photograph name format is "track number-photo number. JPG", and the example shown in fig. 4 shows that 16 aerial photographs of the unmanned aerial vehicle are acquired in the track setting at the time of 162 th time.
(2) Unifying aerial photos in a single folder
Before positioning the longitude and latitude information of the central point of the aerial photo, the aerial photo under all tracks needs to be copied to the same folder. Here, PyCharm is implemented with the following code.
After the code is executed, prompting that' the file directory needing to be copied is input: ", at this point, the" flight "folder and its directory are entered. The execution result is that the aerial photos under all track number folders are finally collected under a folder directory of 'flight' and under a folder named 'total file'.
(3) Extracting central longitude and latitude information of aerial photo
After the aerial photos are summarized, the central longitude and latitude information of all the aerial photos is extracted by utilizing a Python command terminal of ArcGIS, and specific codes of the central longitude and latitude information are as follows.
After the execution is finished, a new folder named as 'SHP' is created in a 'general folder' in a 'flight' folder directory, and a 'UAV _ points.shp' file in the folder contains central longitude and latitude information data of all aerial photos. After the latitude and longitude information of the center points of all aerial photos is extracted, the result includes 11320 aerial photos under 420 tracks.
(4) Matching aerial photos to longitude and latitude information positions
And after the central longitude and latitude information of the aerial photo is obtained, the rest steps are finished in ArcGIS. First, when the step of matching the aerial photograph to the latitude and longitude information position is completed, a previously generated "UAV _ points.shp" file, a satellite image of a research area (in this example, a Sentinel-2 satellite data in the city of guiyang is used as a sample for display), and an own high-definition base map of ArcGIS need to be loaded in the ArcGIS software.
And (4) magnifying to a current screen (a background base image is Sentinel-2) by using a tool in ArcGIS, wherein each latitude and longitude information point of each aerial photo has a respective name (defined according to the naming rule in 2.1), and the part of the aerial photos are 16 photos in the 103 th flight path. Zooming in is continued to a certain photo area (taking 103-8 points as an example), (the background base map is ArcGIS self-contained high-definition base map).
And then adding 103-8 aerial photos into ArcGIS, clicking a Georefferencing tool in the ArcGIS, setting an operation layer in a tool bar as 103-8.JPG, clicking Fit To Display, and displaying the 103-8 aerial photos in the current area. After the operation is completed, the automatic calibration tool in the Georefferenging tool bar is clicked, and if satellite remote sensing data with better quality exist, the geographic correction of the aerial photo can be automatically completed. Generally, ArcGIS with high-definition base map and Sentinel-2 satellite images is difficult to be qualified for automatic correction process, and error prompt is provided. At the moment, the correction points need to be automatically set, and aerial photos can meet the requirements after 3 to 5 correction points are generally set. In this example, 4 correction points are set, and the three corrected road beds are basically registered to the base map.
The above process is to match the aerial photo to the latitude and longitude information position, and at this time, the geographical correction of the example aerial photo is completed.
(5) Identifying and marking surface feature types
After the geographic correction of all aerial photos is completed, the ground object type recognition can be performed on the satellite image map (Sentinel-2 in the present example) with the aerial photos as reference, and the marking is completed. The examples of 103-8.JPG and 103-9.JPG are used herein for the illustration of some operations.
Firstly, a point diagram layer file (namely a point file in a shp type file) is newly created in ArcGIS, then different identifiable points are established by using the newly created point diagram layer file, and assignment is carried out according to different ground object types, so as to complete ground object type identification and marking. The aerial photo can be used as a reference to complete the identification and marking of the types of the buildings in the Sentinel-2. It should be noted that, when the type of the ground feature in the aerial photo is inconsistent with the type of the Sentinel-2 ground feature, the identification and marking of the type of the ground feature are carried out according to the situation.
In summary, the invention relates to 11320 aerial photos, and if the latitude and longitude information of the aerial photos are manually searched one by one and matched with the remote sensing image after being viewed visually, a very complicated process is required, and after the aerial photos are positioned by utilizing the steps of the method, the subsequent operation can be carried out, so that the time can be saved by more than 70%.
The above disclosure is only a few specific embodiments of the present invention, and those skilled in the art can make various modifications and variations of the present invention without departing from the spirit and scope of the present invention, and it is intended that the present invention also include the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
Claims (7)
1. The utility model provides a method for extracting feature points of aerial photograph ground objects based on unmanned aerial vehicle remote sensing technology, which is characterized by comprising the following steps:
acquiring an aerial photo;
extracting the central longitude and latitude information of the aerial photo;
geographically correcting the aerial photo to the position of the longitude and latitude information according to the central longitude and latitude information and the base map information of the aerial photo;
taking the aerial photo after geographic correction as a reference, and identifying the type of the ground object on the satellite image map;
and extracting feature points of the ground features from the aerial photo after the ground feature type identification.
2. The method for extracting feature points of aerial photos of ground objects based on the unmanned remote sensing technology as claimed in claim 1, further comprising:
and naming the aerial photos according to the format of 'track number-photo number-JPG'.
3. The method for extracting feature points of aerial photos of ground objects based on the unmanned remote sensing technology as claimed in claim 1 or 2, further comprising:
and copying the aerial photos in all tracks to the same folder.
4. The method for extracting feature points of aerial photo ground objects based on unmanned aerial vehicle remote sensing technology as claimed in claim 1, wherein the extracting of the central latitude and longitude information of the aerial photo specifically comprises:
determining a field containing the central coordinates of the aerial photograph;
determining geographic coordinates;
and according to the geographic coordinates, extracting the central longitude and latitude information of the aerial photo from a field containing the central coordinates of the aerial photo.
5. The method for extracting the feature points of the aerial photograph ground objects based on the unmanned aerial vehicle remote sensing technology as claimed in claim 4, wherein the step of geographically correcting the aerial photograph to the latitude and longitude information position according to the latitude and longitude information and the base map information of the center of the aerial photograph specifically comprises the steps of:
placing the point with the central longitude and latitude information of the aerial photo in the area with the base map information;
placing the aerial photo corresponding to the central longitude and latitude information of the aerial photo in an area with base map information;
and in the same area with the base map information, geographically correcting the aerial photo to the position of the latitude and longitude information.
6. The method for extracting the feature points of the aerial photos of the ground objects based on the unmanned aerial vehicle remote sensing technology as claimed in claim 5, wherein the identification of the types of the ground objects on the satellite image map is carried out by taking the aerial photos after the geographic correction as reference, and specifically comprises the following steps:
establishing different identification points;
assigning values to the identification points according to different ground feature types;
and taking the aerial photo after the geographic correction as a reference, and carrying out ground object type identification and marking on the satellite image.
7. The method for extracting feature points of aerial photos of ground objects based on the unmanned remote sensing technology as claimed in claim 5, further comprising:
and automatically and geographically correcting the aerial photo to the position of the latitude and longitude information by setting a correction point in the same area with the base map information.
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