CN114638766A - Method for correcting luminous remote sensing image - Google Patents
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Abstract
The invention provides a method for correcting a noctilucent remote sensing image, which comprises the following steps: determining a target area based on the initial noctilucent remote sensing image, and acquiring a road vector diagram of the target area; converting the vector image of the road into raster data from vector data, and taking the raster data as an original luminous remote sensing base image, wherein the resolution ratio of the raster data is the same as that of the image data of the initial luminous remote sensing image; obtaining a luminous remote sensing image, and carrying out visual enhancement processing on the luminous remote sensing image; determining control points on the original luminous remote sensing base map based on the luminous remote sensing image, wherein the control points are used for representing the corresponding positions of the luminous remote sensing image on the original luminous remote sensing base map; and correcting the noctilucent remote sensing image by using an affine transformation model based on the control points. The method comprises the steps of manufacturing an original noctilucent remote sensing correction base map on the basis of road vectors, matching and comparing the obtained noctilucent remote sensing image data with the original noctilucent remote sensing correction base map, correcting the data of the noctilucent remote sensing image, and further realizing high-precision positioning of the noctilucent remote sensing data.
Description
Technical Field
The invention relates to the technical field of noctilucent remote sensing, in particular to a method for correcting a noctilucent remote sensing image.
Background
The luminous remote sensing means can acquire information such as night light and flare, the time width and the information dimension of the earth surface information acquired by optical remote sensing are expanded, and the method is commonly used in the fields of urban development evaluation, human activity prediction, economic index analysis, night fishing boat operation supervision and the like. Compared with optical remote sensing in the daytime, the night light remote sensing only can acquire night brightness information, mainly comprises lamplight, fire light and the like, and contains low richness of image textures, so that the existing day remote sensing technology is difficult to apply to the night light remote sensing image.
The existing noctilucent remote sensing image technology has no processing method aiming at high-precision positioning of the noctilucent remote sensing image, and has no basic control base map data suitable for the noctilucent remote sensing image, the daytime optical remote sensing base map and a manual marking method are often adopted for extracting control information, and the processing efficiency and precision of the noctilucent remote sensing data cannot be guaranteed in large-scale data processing application.
Disclosure of Invention
Aiming at the problems, the invention provides a method for correcting the noctilucent remote sensing image, which is characterized in that an original noctilucent remote sensing correction base map is manufactured on the basis of a road vector, the obtained noctilucent remote sensing image data is matched and compared with the original noctilucent remote sensing correction base map, the data of the noctilucent remote sensing image is corrected, and the high-precision positioning of the noctilucent remote sensing data is further realized.
The method for correcting the noctilucent remote sensing image comprises the following steps: determining a target area based on the initial noctilucent remote sensing image, and acquiring a road vector diagram of the target area; converting the vector image of the road into raster data from vector data to be used as an original luminous remote sensing base image, wherein the resolution ratio of the raster data is the same as that of image data of the initial luminous remote sensing image; obtaining a luminous remote sensing image, and carrying out visual enhancement processing on the luminous remote sensing image; determining control points on the original luminous remote sensing base map based on the luminous remote sensing images, wherein the control points are used for representing corresponding positions of the luminous remote sensing images on the original luminous remote sensing base map; and correcting the noctilucent remote sensing image by using an affine transformation model based on the control points.
Further, in the method for correcting the noctilucent remote sensing image, the determining of the control point on the original noctilucent remote sensing base map based on the noctilucent remote sensing image comprises: dividing the noctilucent remote sensing image into a plurality of blocks, and selecting the blocks with the contrast ratio larger than a first threshold value from the plurality of blocks as feature matching blocks; carrying out template matching on a feature matching block and an original noctilucent remote sensing base image, and determining a first translation amount of the feature matching block relative to the original noctilucent remote sensing base image; and selecting image points of the original noctilucent remote sensing base image with the first translation amount in the first range as control points of the noctilucent remote sensing image.
Further, in the method for correcting the noctilucent remote sensing image, the determining the first translation amount of the feature matching block relative to the original noctilucent remote sensing base map includes: calculating image similarity coefficients of the feature matching block and the original noctilucent remote sensing base map under each translation amount; determining the corresponding translation amount when the image similarity coefficient is maximum as a first translation amount; the image similarity coefficient of the feature matching block and the original noctilucent remote sensing base image under each translation amount is calculated by the following formula:
S coef (u,v) Is an image similarity coefficient, ((ii))x,y) For the original luminous remote sensing base map position coordinates: (u,v) For the feature matching block relative to the original luminous remote sensing base map in (x,y) Amount of translation of (C: (C)x-u,y-v) For feature matching block atx-u,y-v) The gray value of the image Dx,y) For the original luminous remote sensing base picture inx,y) The gray value of the image at (a),μ 1is the mean value of the gray levels of the feature matching blocks,μ 2the gray level mean value of the original noctilucent remote sensing base map is calculated by the following formula:
D(x-u,y-v) For the original luminous remote sensing base picture inx-u,y-v) And the gray value of the image is N, wherein N is the number of pixels of the original noctilucent remote sensing base image.
Further, in the method for correcting the noctilucent remote sensing image, the determining of the control point on the original noctilucent remote sensing base map based on the noctilucent remote sensing image comprises: marking image points of the original noctilucent remote sensing base map with the first translation amount in a first range as first type points; judging whether the first type points accord with integral consistency by adopting an RANSAC algorithm; and taking the first type points which accord with the integral consistency as control points of the noctilucent remote sensing image.
Further, the method for correcting the noctilucent remote sensing image comprises the following steps: and after the night light remote sensing image is corrected, the corrected night light remote sensing image is used for updating the night light remote sensing base map.
Further, in the method for correcting the noctilucent remote sensing image of the present invention, the updating of the noctilucent remote sensing base map by using the corrected noctilucent remote sensing image comprises: and adjusting the pixel gray value of the night remote sensing base map based on the corrected image brightness of the night remote sensing image, wherein for the area without the light reaction in the corrected night remote sensing image, the pixel gray value is reduced at the corresponding position in the night remote sensing base map according to a first proportion, for the area with the light reaction in the corrected night remote sensing image, the pixel gray value is increased at the corresponding position in the night remote sensing base map according to a second proportion, and the second proportion is adjusted based on the sum of the pixel gray values of the corrected image of the night remote sensing image.
Further, in the method for correcting the noctilucent remote sensing image, the step of converting the vector diagram of the road from the vector data to the raster data comprises the following steps: in the road vector map, the grayscale value of the position pixel with the road is set to 255, and the grayscale value of the position pixel without the road is set to 100.
Further, in the method for correcting the noctilucent remote sensing image, the visual enhancement processing of the noctilucent remote sensing image comprises the following steps: stretching the noctilucent remote sensing image by using logarithmic transformation; and stretching the noctilucent remote sensing image within a visual range of 0-255 so as to convert the noctilucent remote sensing image from 16bit data to 8bit data.
Further, in the method for correcting the noctilucent remote sensing image, the formula for stretching the noctilucent remote sensing image by logarithmic transformation is as follows:
wherein, the first and the second end of the pipe are connected with each other,pis the gray value of the original image of the luminous remote sensing image,Lthe method is an image gray value obtained by stretching a noctilucent remote sensing image by using logarithmic transformation.
Further, in the method for correcting the noctilucent remote sensing image, the formula used for stretching the noctilucent remote sensing image in the visual range of 0-255 is as follows:
wherein the content of the first and second substances, Lthe image gray value of the noctilucent remote sensing image after being subjected to logarithmic transformation and stretching treatment,Vthe image gray value is obtained by stretching the noctilucent remote sensing image within the visual range of 0-255.
The method for correcting the noctilucent remote sensing image has the following beneficial effects:
(1) the original luminous remote sensing map is manufactured based on the road vector diagram, the road vector diagram is globally covered and is accurate in position, and the lamplight of the street lamps on two sides of the road has the characteristics of good stability and obvious shape characteristics, so that the street lamps arranged along the road can become important identifiable characteristics in the luminous remote sensing image;
(2) automatic high-precision positioning processing of the noctilucent remote sensing image can be realized through steps of visualization enhancement of the noctilucent image, high-contrast block extraction, template matching processing and the like;
(3) the noctilucent remote sensing base map is updated through iteration of the precisely positioned noctilucent remote sensing image data, so that the noctilucent remote sensing base map data can be gradually optimized, and the high-precision positioning processing of the noctilucent remote sensing image is facilitated.
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FIG. 1 is a schematic flow chart of a method for correcting a noctilucent remote sensing image according to the present invention;
FIG. 2 is a schematic flow chart of a method for determining control points on an original noctilucent remote sensing base map based on a noctilucent remote sensing image according to the present invention;
fig. 3 is a schematic overall flow chart of a method for correcting a noctilucent remote sensing image according to some embodiments of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
Referring to fig. 1, the method for correcting the noctilucent remote sensing image comprises the following steps:
s101, determining a target area based on the initial noctilucent remote sensing image, and acquiring a road vector diagram of the target area.
The noctilucent remote sensing data mainly obtains data such as night light and fire light, generally speaking, the fire light data does not have fixed position attributes, and the street lamp light has the characteristics of good stability and obvious shape characteristics, so that the noctilucent remote sensing data can be used as recognizable characteristics of noctilucent remote sensing images. On the other hand, the street lamp has the characteristic of being arranged along a road, and the light distribution condition of the noctilucent remote sensing image can be fitted by utilizing a road vector diagram.
Based on the method, the road vector diagram is used as the basis of the original luminous remote sensing map. As an important basis for navigation traffic, the road vector data has the characteristics of open source, global coverage and accurate position. The method can directly acquire the global road vector diagram at one time, and determine the road vector diagram of the target area after determining the target area through the initial luminous remote sensing image.
In some embodiments of the invention, according to the spatial positioning information carried by the remote sensing image, the spatial coverage range of the noctilucent remote sensing data is obtained and is marked as the longitude range [ lon1~lon2]Latitude range [ lat1~lat2]. Secondly, determining the longitude extension range to be LonTLatitude extensionRange of LatTThen the spatial range of the target area can be determined to be longitude [ (lon)1-LonT)~(lon2+LonT)]Latitude [ (lat)1-LatT)~(lat2+LatT)]. And determining the road vector diagram of the target area in the road vector diagram by using an area cutting method, namely determining the road vector diagram corresponding to the noctilucent remote sensing image and marking as D'.
And S102, converting the vector image of the road into raster data from the vector data, and using the raster data as an original luminous remote sensing base image, wherein the resolution ratio of the raster data is the same as that of the image data of the initial luminous remote sensing image.
The raster data has simple structure, and is easy to analyze in space and simulate in geographical phenomena, which is beneficial to matching application and analysis with remote sensing data, therefore, the invention converts the road vector diagram of the determined target area into raster data.
According to some embodiments of the invention, the spatial resolution longitude direction of the initial luminous remote sensing image of the target area is RlonIn the direction of latitude RlatThen, the size of the raster data corresponding to the road vector image D' is: transverse direction (lon)2-lon1-2LonT)/ RlonLongitudinal (lat)2-lat1-2LatT)/ Rlat. Therefore, a rasterized image with the same resolution as the original noctilucent remote sensing image can be obtained, and the rasterized image can be used as an original noctilucent remote sensing base image and is marked as D.
According to some embodiments of the invention, in the road vector image, the gray value of the position pixel with the road is set to be 255, and the gray value of the position pixel without the road is set to be 100, so that a raster image with the same resolution as that of the initial noctilucent remote sensing image is obtained. In actual operation, the gray value of the position pixel with the road is set to 255, and the gray value of the position pixel without the road is set to 100, so that the positions with the road and without the road can be more easily distinguished.
S103, obtaining the noctilucent remote sensing image, and carrying out visual enhancement processing on the noctilucent remote sensing image.
The noctilucent remote sensing image mainly acquires data such as night lamplight and fire light, compared with the daytime optical remote sensing image, the gray value of the noctilucent remote sensing image is mostly in a low-brightness area, and the gray level histogram has obvious long tail distribution which is different from common near-Gaussian distribution.
Meanwhile, the remote sensing image is generally stored by 16-bit data, and calculation display and image processing operation are usually carried out in 8-bit data. Therefore, the noctilucent remote sensing image needs to be subjected to visualization enhancement processing, and the noctilucent remote sensing image needs to be converted into 8bit data.
According to some embodiments of the invention, the visual enhancement processing of the luminous remote sensing image comprises: stretching the noctilucent remote sensing image by using logarithmic transformation; and stretching the noctilucent remote sensing image within the visual range of 0-255.
According to some embodiments of the present invention, for the original 16-bit noctilucent remote sensing image, in order to increase the recognition degree of the luminance information, a logarithmic function is adopted to perform stretching processing, as shown in the following formula:
wherein, the first and the second end of the pipe are connected with each other,pis the gray value of the original image of the luminous remote sensing image,Lthe method is an image gray value obtained by stretching a noctilucent remote sensing image by using logarithmic transformation. lg (×) is the operation of taking the logarithm and max (×) is the operation of taking the maximum.
And then, the image graph L after logarithmic transformation is further extended to a visual range of 0-255, so that 8bit luminous remote sensing data is obtained, and the following formula is shown:
wherein the content of the first and second substances, Lthe image gray value of the noctilucent remote sensing image after being subjected to logarithmic transformation and stretching treatment,Vthe image gray value is obtained by stretching the noctilucent remote sensing image within the visual range of 0-255. max (×) is a maximum operation and min (×) is a minimum operation.
According to some embodiments of the invention, the visual enhancement processing on the noctilucent remote sensing image can also use methods based on logarithmic stretching, histogram stretching, exponential stretching and the like.
And S104, determining control points on the original noctilucent remote sensing base map based on the noctilucent remote sensing images, wherein the control points are used for representing corresponding positions of the noctilucent remote sensing images on the original noctilucent remote sensing base map.
Referring to fig. 2, according to some embodiments of the present invention, determining a control point on an original night light remote sensing base map based on a night light remote sensing image includes:
s1041, dividing the noctilucent remote sensing image into a plurality of blocks, and selecting the block with the contrast ratio larger than a first threshold value from the plurality of blocks as a feature matching block.
The luminous remote sensing image only obtains information such as high-brightness lamplight and fire light at night, and most of areas in the whole scene image have no useful information. Therefore, in order to improve the stability and the processing efficiency of subsequent template matching, the noctilucent remote sensing data is firstly divided into discrete block regions, and then the contrast of the block regions is calculated one by one, as shown in the following formula:
wherein the content of the first and second substances,δ( i , j )=| i – j l, which is the gray difference between adjacent pixels; P δ ( i , j ) Is a gray value between vector pixels ofδThe probability of pixel distribution.
According to some embodiments of the invention, the pixel neighborhood rule used is four-neighbor or eight-neighbor.
The higher the contrast of the block is, the richer the identifiable information contained in the image is, and the more suitable the image is for template matching processing. Therefore, some embodiments of the present invention use the blocks with contrast greater than the first threshold as feature matching blocks for subsequent template matching.
According to some embodiments of the present invention, in the feature matching block extraction, a matchable degree of the feature matching block may be calculated in a contrast manner, or may be measured by using methods such as a gray standard deviation and a texture richness.
And S1042, carrying out template matching on the feature matching block and the original noctilucent remote sensing base map, and determining a first translation amount of the feature matching block relative to the original noctilucent remote sensing base map.
According to some embodiments of the invention, determining the first translation amount of the feature matching block relative to the original luminous remote sensing base map comprises the following steps: calculating image similarity coefficients of the feature matching block and the original noctilucent remote sensing base map under each translation amount; determining the corresponding translation amount when the image similarity coefficient is maximum as a first translation amount; the image similarity coefficient of the feature matching block and the original noctilucent remote sensing base image under each translation amount is calculated by the following formula:
S coef (u,v) Is an image similarity coefficient, ((ii))x,y) For the original luminous remote sensing base map position coordinates: (u,v) For the feature matching block relative to the original luminous remote sensing base map in (x,y) Amount of translation of (C: (C)x-u,y-v) For feature matching block atx-u,y-v) The gray value of the image Dx,y) For the original luminous remote sensing base picture inx,y) The gray value of the image at (a),μ 1is the mean value of the gray levels of the feature matching blocks,μ 2the gray level mean value of the original noctilucent remote sensing base map is calculated by the following formula:
D(x-u,y-v) For the original luminous remote sensing base picture inx-u,y-v) The gray value of the image is N which is the original luminous remote sensing base mapThe number of pixels.
S1043, selecting image points of the original noctilucent remote sensing base map with the first translation amount in the first range as control points of the noctilucent remote sensing image.
The control points are used for representing the corresponding positions of the luminous remote sensing images on the original luminous remote sensing base map. When the feature matching block is matched with the original luminous remote sensing base map in a template mode, corresponding position data of different point data of the feature matching block on the original luminous remote sensing base map can be determined. When different data are used for template matching, the corresponding position data of the determined noctilucent remote sensing image on the original noctilucent remote sensing base map are different.
In order to reduce the error during determination, some embodiments of the present invention may select, when calculating the first translation amount of the feature matching block relative to the original noctilucent remote sensing base map, an image point of the original noctilucent remote sensing base map with the first translation amount within a first range as a control point of the noctilucent remote sensing image. That is to say, when the calculated first translation amount is within the first range, the actual position of the noctilucent remote sensing image determined on the original noctilucent remote sensing base map is more accurate.
Some embodiments of the present invention may obtain a matching result of the feature matching block matching with the original noctilucent remote sensing base map template, and then obtain an optimal matching position by using a RAndom SAmple Consensus (RAndom SAmple Consensus) algorithm according to the multiple matching results, so as to reduce an error in determining the position.
The method comprises the following specific steps: marking image points of the original noctilucent remote sensing base map with the first translation amount in a first range as first type points; judging whether the first type of points accord with the integral consistency by adopting an RANSAC algorithm; and taking the first type points which accord with the integral consistency as control points of the noctilucent remote sensing image.
And S105, correcting the noctilucent remote sensing image by using the affine transformation model based on the control points.
After the control points are selected in the steps, the noctilucent remote sensing image can be corrected by using an affine transformation model based on the control points.
More specifically, some embodiments of the present invention adopt the following affine transformation model equation to correct the noctilucent remote sensing image:
wherein (A), (B), (C), (D), (C), (B), (C)X，Y) For control point ground coordinates: (x，y) Is the corresponding coordinate on the luminous remote sensing image map,a 0、a 1、a 2、a 3、b 0、b 1、b 2、b 3affine transformation model parameters.
When a plurality of control point data are acquired, for example, the number of the acquired control points is not less than 4, the parameters of each affine transformation model can be obtained by using the least square method.
And then coordinate transformation and resampling are carried out, so that the correction of the noctilucent remote sensing image can be realized by utilizing the affine transformation model equation, and accurate positioning data can be obtained.
Because there is no accurate night light remote sensing base map data, some embodiments of the invention can update the night light remote sensing base map by using the corrected accurately positioned night light remote sensing image data in order to improve the correction accuracy of the night light remote sensing base map and ensure the continuous iteration update of the night light remote sensing base map.
According to some embodiments of the invention, updating the night light remote sensing base map by using the corrected night light remote sensing image comprises: and adjusting the pixel gray value of the night remote sensing base map based on the corrected image brightness of the night remote sensing image, wherein for the area without the light reaction in the corrected night remote sensing image, the pixel gray value is reduced at the corresponding position in the night remote sensing base map according to a first proportion, for the area with the light reaction in the corrected night remote sensing image, the pixel gray value is increased at the corresponding position in the night remote sensing base map according to a second proportion, and the second proportion is adjusted based on the sum of the pixel gray values of the corrected image of the night remote sensing image.
According to some embodiments of the invention, the calculation may be performed according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,D nfor the updated night-light remote sensing base map gray value,Cfor the corrected gray value of the luminous remote sensing image,Dand max (x) is the maximum value operation for the gray value of the luminous remote sensing base map before updating.
Through the processing, the night light remote sensing base map can be updated according to the continuously accumulated night light remote sensing data, the gray value of the high-brightness area at night is increased, the gray value of the non-brightness reaction area is reduced, and the result is gradually close to the real night remote sensing acquisition result.
Fig. 3 is a schematic overall flow chart of a method for correcting a noctilucent remote sensing image according to some embodiments of the present invention. In the embodiments, the invention realizes the automatic accurate positioning of the noctilucent remote sensing data, obtains more accurate noctilucent remote sensing base map data through the accumulation of noctilucent remote sensing observation data, can gradually optimize the noctilucent remote sensing base map data, and is beneficial to the high-accuracy positioning processing of noctilucent remote sensing images.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for correcting a luminous remote sensing image is characterized by comprising the following steps:
determining a target area based on the initial luminous remote sensing image, and acquiring a road vector diagram of the target area;
converting the road vector image from vector data into raster data serving as an original luminous remote sensing base map, wherein the raster data has the same resolution as the image data of the initial luminous remote sensing image;
obtaining a luminous remote sensing image, and carrying out visual enhancement processing on the luminous remote sensing image;
determining control points on the original luminous remote sensing base map based on the luminous remote sensing image, wherein the control points are used for representing the corresponding positions of the luminous remote sensing image on the original luminous remote sensing base map;
and correcting the noctilucent remote sensing image by using an affine transformation model based on the control points.
2. The method for correcting the luminous remote sensing image according to claim 1, wherein the determining a control point on the original luminous remote sensing base map based on the luminous remote sensing image comprises:
dividing the noctilucent remote sensing image into a plurality of blocks, and selecting the blocks with the contrast ratio larger than a first threshold value from the blocks as feature matching blocks;
carrying out template matching on the feature matching block and the original luminous remote sensing base map, and determining a first translation amount of the feature matching block relative to the original luminous remote sensing base map;
and selecting image points of the original noctilucent remote sensing base image with the first translation amount in a first range as control points of the noctilucent remote sensing image.
3. The method for correcting the noctilucent remote sensing image according to claim 2, wherein the determining the first amount of translation of the feature matching block relative to the original noctilucent remote sensing base map comprises:
calculating image similarity coefficients of the feature matching block and the original noctilucent remote sensing base map under each translation amount;
determining the corresponding translation amount when the image similarity coefficient is maximum as a first translation amount; wherein the content of the first and second substances,
the image similarity coefficient of the feature matching block and the original noctilucent remote sensing base image under each translation amount is calculated by adopting the following formula:
S coef (u,v) Is an image similarity coefficient, ((ii))x,y) For the original luminous remote sensing base map position coordinates: (u,v) For the feature matching block relative to the original luminous remote sensing base map (B) ((B))x,y) Amount of translation of (A), (B), (C)x-u,y-v) For the feature matching block in (x-u,y-v) The gray value of the image Dx,y) For the original luminous remote sensing base map (B) < CHEM >x,y) The gray value of the image at (a),μ 1is the gray level mean of the feature matching block,μ 2the gray level mean value of the original noctilucent remote sensing base map is calculated by the following formula:
D(x-u,y-v) For the original luminous remote sensing base map (B) < CHEM >x-u,y-v) And N is the number of pixels of the original noctilucent remote sensing base map.
4. The method for correcting the luminous remote sensing image according to claim 2, wherein the determining a control point on the original luminous remote sensing base map based on the luminous remote sensing image comprises:
marking image points of the original noctilucent remote sensing base map with the first translation quantity in a first range as first type points;
judging whether the first type points accord with integral consistency by adopting an RANSAC algorithm;
and taking the first type points which accord with the integral consistency as control points of the noctilucent remote sensing image.
5. A method for correcting a luminous remote sensing image according to claim 1, comprising:
and after the noctilucent remote sensing image is corrected, updating the noctilucent remote sensing base map by using the corrected noctilucent remote sensing image.
6. The method for correcting the noctilucent remote sensing image according to claim 5, wherein the updating the noctilucent remote sensing base map by using the corrected noctilucent remote sensing image comprises:
adjusting the pixel gray value of the luminous remote sensing base map based on the corrected image brightness of the luminous remote sensing image, wherein,
for the area without luminous reaction in the corrected luminous remote sensing image, the pixel gray value is reduced at the corresponding position in the luminous remote sensing base map according to a first proportion,
and for the area with the bright reaction in the corrected noctilucent remote sensing image, increasing the pixel gray value at the corresponding position in the noctilucent remote sensing base map according to a second proportion, wherein the second proportion is adjusted based on the sum of the pixel gray values of the corrected noctilucent remote sensing image.
7. A method for correcting a luminous remote sensing image according to claim 1, wherein the step of converting the road vector diagram from vector data to raster data comprises:
in the road vector map, the gray value of the position pixel with the road is set to 255, and the gray value of the position pixel without the road is set to 100.
8. The method for correcting the noctilucent remote sensing image according to claim 1, wherein the performing of the visualization enhancement processing on the noctilucent remote sensing image includes:
stretching the noctilucent remote sensing image by using logarithmic transformation;
and stretching the noctilucent remote sensing image within a visual range of 0-255 so as to convert the noctilucent remote sensing image from 16bit data to 8bit data.
9. A method for correcting a luminous remote sensing image according to claim 8, wherein the formula for stretching the luminous remote sensing image by using logarithmic transformation is as follows:
wherein the content of the first and second substances,pis the gray value of the original image of the luminous remote sensing image,Land the image gray value is obtained by stretching the noctilucent remote sensing image by using logarithmic transformation.
10. A method for correcting a luminous remote sensing image according to claim 8, wherein the formula for stretching the luminous remote sensing image within a visual range of 0-255 is as follows:
wherein the content of the first and second substances, Lthe image gray value of the noctilucent remote sensing image after being subjected to stretching processing through logarithmic transformation,Vthe image gray value is obtained after stretching processing is carried out on the noctilucent remote sensing image within the visual range of 0-255.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116630204A (en) * | 2023-07-19 | 2023-08-22 | 南京佳格耕耘科技有限公司 | Remote sensing image online analysis processing system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105930867A (en) * | 2016-04-19 | 2016-09-07 | 中国科学院遥感与数字地球研究所 | Data mining method based on time series luminous remote sensing image |
| US20170039436A1 (en) * | 2015-08-03 | 2017-02-09 | Nokia Technologies Oy | Fusion of RGB Images and Lidar Data for Lane Classification |
| CN106920235A (en) * | 2017-02-28 | 2017-07-04 | 中国科学院电子学研究所 | Star-loaded optical remote sensing image automatic correction method based on the matching of vector base map |
| CN107341781A (en) * | 2017-07-20 | 2017-11-10 | 中国科学院电子学研究所 | Based on the SAR image correcting methods for improving the matching of phase equalization characteristic vector base map |
| CN109977344A (en) * | 2019-03-20 | 2019-07-05 | 武汉大学 | A kind of block adjustment method of spaceborne noctilucence remote sensing image |
| CN113610873A (en) * | 2021-10-09 | 2021-11-05 | 中国科学院地理科学与资源研究所 | Noctilucent remote sensing image data processing method and device |
| CN113723175A (en) * | 2021-07-14 | 2021-11-30 | 中国人民解放军战略支援部队信息工程大学 | Method for extracting urban built-up area of remote sensing image |
-
2022
- 2022-04-08 CN CN202210364692.2A patent/CN114638766B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170039436A1 (en) * | 2015-08-03 | 2017-02-09 | Nokia Technologies Oy | Fusion of RGB Images and Lidar Data for Lane Classification |
| CN105930867A (en) * | 2016-04-19 | 2016-09-07 | 中国科学院遥感与数字地球研究所 | Data mining method based on time series luminous remote sensing image |
| CN106920235A (en) * | 2017-02-28 | 2017-07-04 | 中国科学院电子学研究所 | Star-loaded optical remote sensing image automatic correction method based on the matching of vector base map |
| CN107341781A (en) * | 2017-07-20 | 2017-11-10 | 中国科学院电子学研究所 | Based on the SAR image correcting methods for improving the matching of phase equalization characteristic vector base map |
| CN109977344A (en) * | 2019-03-20 | 2019-07-05 | 武汉大学 | A kind of block adjustment method of spaceborne noctilucence remote sensing image |
| CN113723175A (en) * | 2021-07-14 | 2021-11-30 | 中国人民解放军战略支援部队信息工程大学 | Method for extracting urban built-up area of remote sensing image |
| CN113610873A (en) * | 2021-10-09 | 2021-11-05 | 中国科学院地理科学与资源研究所 | Noctilucent remote sensing image data processing method and device |
Non-Patent Citations (2)
| Title |
|---|
| JINHU BIAN 等: ""Inter-Calibration of Nighttime Light Data Between DMSP/OLS And NPP/VIIRS in the Economic Corridors of Belt And Road Initiative"", 《IEEE》 * |
| 李明峰 等: ""NPP/VIIRS多时相夜光遥感影像校正方法"", 《测绘通报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116630204A (en) * | 2023-07-19 | 2023-08-22 | 南京佳格耕耘科技有限公司 | Remote sensing image online analysis processing system |
| CN116630204B (en) * | 2023-07-19 | 2023-09-26 | 南京佳格耕耘科技有限公司 | Remote sensing image online analysis processing system |
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