CN106650673A - Urban mapping method and device - Google Patents
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Abstract
The invention discloses an urban mapping method and device. The urban mapping method comprises the following steps of receiving a remote sensing image of a target city input by a user and obtaining remote sensing image data; obtaining a target vegetation index of the remote sensing image based on the remote sensing image data; receiving original light data of the target city input by the user; carrying out normalization and re-sampling processing on the original light data to obtain target light data; obtaining a city index based on the target vegetation index and the target light data; and carrying out mapping based on the city index to obtain a city effect drawing of the target city. The urban mapping method has simple and convenient processing process, high automation degree and high processing efficiency and does not need a complicated preprocessing process; and furthermore, the city index is obtained based on the target vegetation index and the target light data, the city index is utilized to carry out mapping, the mapping effect is good, the precision is high and the method is easy to popularize and apply.
Description
Technical field
The present invention relates to remote sensing application technical field, more particularly to a kind of urban mapping method and device.
Background technology
City, due to population and the concentration of resource, becomes the research emphasis of research Global climate change and mankind's activity.To the greatest extent
Pipe city floor space is only 2%, but it but occupies 50% population, generates 90% GDP, has discharged 70% temperature
Room gas, in order to preferably monitor impact of the urban development to the mankind, therefore studies large scale scope and high-precision monitoring water
It is flat, become a focus search problem.
Due to the wide coverage of remote sensing image, therefore become the significant data source for monitoring urban development, low spatial
Resolution remote sense image can cover large area, but because its spatial resolution is low, and cannot show the details in city
Part.Be currently based on the urbanization research of remote sensing image mainly have it is following have it is to be strengthened where：The definition in city is failed to understand
Really, the spatial resolution of remote sensing image is too low, and existing algorithm effect is not enough.
Landsat remote sensing images have higher spatial resolution (30m) due to it, therefore become Studying Urbanization
Emphasis data source, but Landsat remote sensing images simply use optical image, it is difficult to improve same object different images, the different spectrum phenomenon of jljl, such as
The problems such as bare area and building, shade and water body, correlative study person is proposed and is normalized not based on biomass component index, multi-source
The processing method that the indexes such as permeable index are processed.But based in the processing method that biomass component index is processed,
Pretreatment stage needs to enter water body line mask, and pre-treatment step is more, and complex operation has a strong impact on computational efficiency.Based on many
Source is normalized in the processing method that impermeable aqua index is processed, and it is less that it has a night image coverage, and utilizes earth's surface
The shortcomings of temperature error is larger, causes the city map pixel accuracy for building low, it is impossible to show City Details part.
The content of the invention
The technical problem to be solved in the present invention is, for the defect of prior art, there is provided a kind of urban mapping method and
Device.
The technical solution adopted for the present invention to solve the technical problems is：A kind of urban mapping method, comprises the steps：
The remote sensing image of the target cities of receiving user's input, obtains remote sensing image data；
Based on the remote sensing image data, the target vegetation index of the remote sensing image is obtained；
The original light data of the target cities of receiving user's input；
The original light data is normalized and resampling is processed, obtain target light data；
Based on the target vegetation index and the target light data, city index is obtained；
Charted based on the city index, obtained the city design sketch of the target cities.
Preferably, the remote sensing image data includes near infrared band and red spectral band；
It is described that the target vegetation index of the remote sensing image is obtained based on the remote sensing image data, including：Using vegetation
Formula of index is processed the remote sensing image data, obtains the target vegetation index of the remote sensing image；
Wherein, the vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI refers to for target vegetation
Number, NIR is near infrared band, and R is red spectral band.
Preferably, it is described that the target vegetation index of the remote sensing image is obtained based on the remote sensing image data, afterwards also
Including：
Using the target vegetation index of same pixel at least three remote sensing images of the target cities, the picture is built
The target vegetation index sequence of unit；
The target vegetation index sequence of each pixel is synthesized using default composition rule, obtains the pixel
Synthesis vegetation index；
Based on the synthesis vegetation index of each pixel, the target vegetation index of the remote sensing image is updated；
Wherein, the default composition rule includes：Obtain the maximum and most of the target vegetation index sequence of the pixel
Little value；If the maximum be more than first threshold, using the maximum as the pixel synthesis vegetation index；If described
Minimum of a value be less than Second Threshold, then using the minimum of a value as the pixel synthesis vegetation index；If the maximum is little
In and/or the minimum of a value be not more than the Second Threshold, then using the intermediate value of the target vegetation index sequence as the picture
The synthesis vegetation index of unit.
Preferably, it is described the original light data to be normalized and resampling process, target light data is obtained,
Including：
The original light data is normalized using linear normalization formula, obtains normalization light number
According to；
Resampling is carried out to the normalization light data using closest distribution method, to obtain and remote sensing image tool
There is the target light data of same spatial resolution；
Wherein, the linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalizing
Change light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
Preferably, it is described based on the target vegetation index and the target light data, city index is obtained, including：
The target vegetation index and the target light data are processed using city index construction formula, to obtain
Take the city index；
Wherein, the city index construction formula includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city
Index, NTL is target light data, and NDVI is target vegetation index.
The present invention also provides a kind of urban mapping device, including：
Remote sensing image data acquisition module, for the remote sensing image of the target cities of receiving user's input, obtains remote sensing shadow
As data；
Target vegetation index acquisition module, for based on the remote sensing image data, obtaining the target of the remote sensing image
Vegetation index；
Original light data receiver module, for the original light data of the target cities of receiving user's input；
Target light data acquisition module, for the original light data is normalized and resampling process, obtain
Take target light data；
City index acquisition module, for based on the target vegetation index and the target light data, obtaining city
Index；
Urban mapping processing module, for being charted based on the city index, obtains the city of the target cities
Design sketch.
Preferably, the remote sensing image data includes near infrared band and red spectral band；
The target vegetation index acquisition module, for being entered to the remote sensing image data using vegetation index computing formula
Row is processed, and obtains the target vegetation index of the remote sensing image；
Wherein, the vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI refers to for target vegetation
Number, NIR is near infrared band, and R is red spectral band.
Preferably, the target vegetation index acquisition module includes：
Vegetation index sequence construct unit, for same pixel at least three remote sensing images using the target cities
Target vegetation index, build the target vegetation index sequence of the pixel；
Synthesis vegetation index acquiring unit, for using default target vegetation index of the composition rule to each pixel
Sequence is synthesized, and obtains the synthesis vegetation index of the pixel；
Target vegetation index updating block, for the synthesis vegetation index based on each pixel, updates the remote sensing
The target vegetation index of image；
Wherein, the default composition rule includes：Obtain the maximum and most of the target vegetation index sequence of the pixel
Little value；If the maximum be more than first threshold, using the maximum as the pixel synthesis vegetation index；If described
Minimum of a value be less than Second Threshold, then using the minimum of a value as the pixel synthesis vegetation index；If the maximum is little
In and/or the minimum of a value be not more than the Second Threshold, then using the intermediate value of the target vegetation index sequence as the picture
The synthesis vegetation index of unit.
Preferably, the target light data acquisition module includes：
Normalized unit, for being normalized place to the original light data using linear normalization formula
Reason, obtains normalization light data；
Resampling processing unit, for carrying out resampling to the normalization light data using closest distribution method, with
Obtain the target light data that there is same spatial resolution with the remote sensing image；
Wherein, the linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalizing
Change light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
Preferably, the city index acquisition module, for being referred to the target vegetation using city index construction formula
Number and the target light data are processed, to obtain the city index；
Wherein, the city index construction formula includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city
Index, NTL is target light data, and NDVI is target vegetation index.
The present invention has the advantage that compared with prior art：In urban mapping method and device provided by the present invention,
Target vegetation index is obtained based on remote sensing image, original light data is obtained and is obtained target light data, recycle target vegetation
Index and target light data obtain city index, are charted using city index.In the urban mapping method and device, place
Reason process is simple and convenient, high degree of automation, and treatment effeciency is high, the preprocessing process without the need for carrying out complexity；And based on target
Vegetation index and target light data obtain city index, are charted using city index, and mapping effect is good, high precision, easily
In promoting the use of.
Description of the drawings
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing：
Fig. 1 is a flow chart of urban mapping method in the embodiment of the present invention 1；
Fig. 2 is a concrete schematic diagram of urban mapping method in the embodiment of the present invention 1；
Fig. 3 is a theory diagram of urban mapping device in the embodiment of the present invention 2.
Specific embodiment
In order to be more clearly understood to the technical characteristic of the present invention, purpose and effect, now compare accompanying drawing and describe in detail
The specific embodiment of the present invention.
Embodiment 1
Fig. 1 illustrates the flow chart of the urban mapping method in the present embodiment.The urban mapping method is based on as vegetation refers to
Number is reduced, the phenomenon of urban lighting data increase, by processing vegetation index and light data, you can obtain target city
The city index in city, is charted based on city index, you can obtain the city design sketch of target cities.As shown in figure 1, the city
City's drafting method comprises the steps：
S10：The remote sensing image of the target cities of receiving user's input, obtains remote sensing image data.
Wherein, the remote sensing image can be middle low resolution remote sensing image, because middle low resolution remote sensing image has shadow
As broad covered area and cheap advantage so as to apply and there is broad covered area and cheap in urban mapping,
The cost that charts can effectively be reduced.
In the present embodiment, the landsat7ETM+ of receiving user's input this intermediate-resolution remote sensing image, landsat7ETM
+ remote sensing image has higher spatial resolution (30m), during ensureing to be charted based on landsat7ETM+ remote sensing images,
The precision of the city design sketch of obtained target cities is higher.Wherein, landsat7 is the land spy of U.S.'s transmitting in 1999
Satellite system is surveyed, Enhanced Thematic Mapper Plus (referred to as ETM+, the special drawing equipment of enhancement mode) is equipped with and is set
Standby, solar radiation that ETM+ feelings of passivity answer earth surface reflection and the heat radiation for distributing have the inductor of 8 wave bands, cover infrared
To the different wavelength range of visible ray.ETM+ equipment is higher in the resolution ratio of infrared band, with higher accuracy, so that most
Whole city design sketch has higher precision.Because ETM+ remote sensing image feelings of passivity answer the solar radiation of earth surface reflection and distribute
Heat radiation, based on ETM+ remote sensing images obtain remote sensing image data when, atmospheric correction need to be carried out, to obtain real earth's surface
Reflectivity.It is to be appreciated that remote sensing image data can also be Sentinal-2A images or other images.
Because the urban mapping method is charted based on vegetation index and urban lighting data so that in the remote sensing image
Should include that the remote sensing image data of vegetation index can be affected.In the present embodiment, remote sensing image data includes near infrared band and red
Optical band.Near infrared light (Near Infrared, NIR) wave band is between visible ray (VI S) and mid-infrared light (MIR)
Electromagnetic wave, by ASTM (testing and material tests association in the U.S.) definition electromagnetic wave of the wavelength in the range of 780～2526nm is referred to,
Traditionally again near infrared region is divided into near-infrared shortwave (780～1100nm) and near-infrared long wave (1100～2526nm) two
Region.Near infrared region is the non-visible region that people have found earliest.Red spectral band is the part in visible light wave range, can
The wave-length coverage of optical band is seen between 0.77～0.622 micron, can be used to calculate vegetation index.
S20：Based on remote sensing image data, the target vegetation index of remote sensing image is obtained.
Wherein, target vegetation index is used to recognize the vegetative coverage situation in remote sensing image that target vegetation index to be bigger, its
The vegetation of covering is more, can be based on the vegetative coverage situation that target vegetation index determines target cities.
Step S20 is specifically included：Remote sensing image data is processed using vegetation index computing formula, obtains remote sensing shadow
The target vegetation index of picture；
Wherein, vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI be target vegetation index, NIR
For near infrared band, R is red spectral band.(Normalized Difference Vegetation Index, it is poor to normalize for NDVI
Divide vegetation index), for detecting vegetation generation, vegetation coverage and eliminating the fields such as partial radiation error.Wherein, NIR and R point
Do not refer to the reflectance value of near infrared band and red spectral band.
Further, between [- 1,1], negative value represents that covered ground is cloud, water, snow etc. to the value of NDVI, to visible ray
High reflection；0 indicates rock or exposed soil etc., NIR and R approximately equals；On the occasion of, vegetative coverage is indicated, and increase with coverage
And increase.It is to be appreciated that target vegetation index NDVI is a normalization data, its data acquisition makes between [- 1,1]
When must be processed using target vegetation index NDVI, fast convergence rate can accelerate data-handling efficiency.
When being charted using the target vegetation index of single remote sensing image, target vegetation index and the city for ultimately forming
City's design sketch is associated, and because target vegetation index can affect the precision of city design sketch, therefore need to ensure target vegetation index
Accuracy rate.Therefore, in the urban mapping method, after step S20, also comprise the steps：
S21：Using the target vegetation index of same pixel at least three remote sensing images of target cities, pixel is built
Target vegetation index sequence.
It is to be appreciated that the target cities covered at least three remote sensing images are identical, it is identical in each remote sensing image
Pixel points to same position in target cities.The target of same pixel is planted at least three remote sensing images using target cities
By index, when building the target vegetation index sequence of pixel, the target vegetation index of same pixel is sorted according to time sequencing,
To form the target vegetation index sequence of the pixel.It is to be appreciated that at least three remote sensing images are according to certain time interval
Collection, it is such as annual or monthly.In the present embodiment by taking year as an example, if the target vegetation index of First Year some day of pixel A is
0.32, Second Year target vegetation index on the same day is 0.40, and target vegetation index on the same day is 0.45 within the 3rd year, then the picture
The target vegetation index sequence of first A is { 0.32,0.4,0.45 }.
S22：The target vegetation index sequence of each pixel is synthesized using default composition rule, obtains the conjunction of pixel
Into vegetation index.
Wherein, default composition rule includes：Obtain the maximum and minimum of a value of the target vegetation index sequence of pixel；If most
Big value is more than first threshold, then using maximum as pixel synthesis vegetation index；If minimum of a value is less than Second Threshold, will most
Synthesis vegetation index of the little value as pixel；If maximum is not more than and/or minimum of a value is not more than Second Threshold, target is planted
By the intermediate value of exponential sequence as pixel synthesis vegetation index.It is to be appreciated that using default composition rule to each pixel
Target vegetation index sequence synthesized, multiple target vegetation indexs in target vegetation index sequence can be converted into one most
The synthesis vegetation index of the vegetative coverage degree of the corresponding target cities of pixel can be embodied.The span of target vegetation index exists
Between [- 1,1], whether it is vegetation area that first threshold is used to limit, and whether it is water area that Second Threshold is used to limit.If mesh
Mark vegetation index is more than first threshold, then the corresponding region of target vegetation index is vegetation area；If target vegetation index is little
In Second Threshold, then the corresponding region of target vegetation index is water area；If target vegetation index is in first threshold and
Between two threshold values, then the corresponding region of target vegetation index is bare area or city.
In the present embodiment, if first threshold is 0.4, Second Threshold is -0.2；If the target vegetation index sequence of pixel A is
{ 0.55,0.58,0.49,0.45,0.50 }, the target vegetation index sequence of pixel B for -0.1, -0.15, -0.2, -0.18, -
0.22 }, the target vegetation index sequence of pixel C is { 0.13,0.11,0.15,0.2,0.22 }.Enter according to the default composition rule
Row synthesis is processed, then the synthesis vegetation index of pixel A is 0.58, and the synthesis vegetation index of pixel B is -0.22, the synthesis of pixel C
Vegetation index is 0.15.
S23：Based on the synthesis vegetation index of each pixel, the target vegetation index of remote sensing image is updated.
Each remote sensing image includes multiple pixels, and the synthesis vegetation index of each pixel is updated in remote sensing image accordingly
The target vegetation index of pixel, to update the target vegetation index of remote sensing image, and the target based on remote sensing image after renewal is planted
Urban mapping is carried out by index, makes the precision of city design sketch for ultimately forming higher, can apparent display City Details part.
S30：The original light data of the target cities of receiving user's input.
Because the realization of the urban mapping method is based on as vegetation index is reduced, what urban lighting data increased shows
As, therefore the original light data of receiving user's input target cities is needed, and original light data is processed, to realize city
The purpose of city's drawing.
S40：Original light data is normalized and resampling is processed, obtain target light data.
Original light data is normalized, original light data is limited in preset range, after facilitating
It is continuous to carry out data processing, Data Convergence speed is improved, improve data-handling efficiency.Due to carrying out normalizing to original light data
After change is processed, Pixel size may be caused undesirable, need to make different pixels using identical resolution ratio, therefore need to be carried out
Resampling is processed, to ensure that each pixel of the target light data for getting has identical spatial resolution, so that subsequently
The process convenience of calculation of city index is obtained based on target light data, accelerates convergence rate, improved.
Step S40 is specifically included：
S41：Original light data is normalized using linear normalization formula, obtains normalization light number
According to.
Wherein, linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalization lamp
Light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
It is to be appreciated that the original light data of the target cities of user input has maximum and minimum of a value, by target
The original light data RNTL of each pixel in city is normalized, to obtain the normalization light data of the pixel
NTL', and then the normalization light data of target cities is obtained, follow-up urban mapping is carried out using the normalization light data, can
Accelerate Data Convergence speed to a certain extent, improve data-handling efficiency.
S42：Resampling is carried out to normalizing light data using closest distribution method, there is phase with remote sensing image to obtain
The target light data of isospace resolution ratio.
Wherein, closest distribution method is, for the resampling technique of discrete (classification) data, will not to change input block
Value.The position of unit center in output grid data set is navigated to after input raster, closest distribution method will determine input grid
Unit center position nearest on lattice simultaneously the value of the unit is distributed to the unit on output grid.In the present embodiment, using most
Neighbouring distribution method carries out resampling to normalizing light data, to obtain the target with remote sensing image with same spatial resolution
Light data, so as to subsequently obtain the process convenience of calculation of city index based on target light data, further speeds up convergence speed
Degree, improves treatment effeciency.
S50：Based on target vegetation index and target light data, city index is obtained.
Due to existing in remote sensing image as vegetation index is reduced, the phenomenon of urban lighting data increase, based on the phenomenon
Vegetation index and the incidence relation between light data and city index are obtained, the incidence relation is recycled to target vegetation index
Processed with target light data, you can obtain city index, can be charted using the city index.
Step S50 is specifically included：Using city index construction formula to target vegetation index and the target light data
Processed, to obtain city index.
Wherein, index construction formula in city includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city index,
NTL is target light data, and NDVI is target vegetation index.
It is to be appreciated that target light data be original light data is normalized and resampling process result,
When obtaining city index using target light data, have the advantages that fast convergence rate, treatment effeciency are high.Target vegetation index
NDVI is a normalization data, and its data acquisition is between [- 1,1] so that at target vegetation index NDVI
During reason, fast convergence rate can accelerate data-handling efficiency.
S60：Charted based on city index, obtained the city design sketch of target cities.
Because city index and target vegetation index and target night lights correlation of indices join so that entered based on city index
The pixel accuracy of city design sketch of the row drawing to be formed is high, can clearly show that City Details part.
Fig. 2 illustrates a concrete schematic diagram of urban mapping method in the present embodiment.As shown in Fig. 2 based on target cities
Remote sensing image data, the target vegetation index (NDVI) of acquisition is as shown in A figures in Fig. 2；Original light data is normalized
Process with resampling, the target light data (NTL) of acquisition is as shown in B figures in Fig. 2；Based on target vegetation index (NDVI) and mesh
Beacon light light data (NTL), acquisition city index (NDUI) is as shown in C figures in Fig. 2；Charted based on city index, obtained mesh
In the city design sketch such as Fig. 2 in mark city shown in D figures.
In the urban mapping method that the present embodiment is provided, target vegetation index is obtained based on remote sensing image, obtained original
Light data obtains target light data, recycles target vegetation index and target light data to obtain city index, using city
City's index is charted.In the urban mapping method, processing procedure is simple and convenient, high degree of automation, and treatment effeciency is high, without the need for
Carry out the preprocessing process of complexity；And city index is obtained based on target vegetation index and target light data, using city
Index is charted, and mapping effect is good, high precision, it is easy to promote the use of.
Embodiment 2
Fig. 3 illustrates the theory diagram of the urban mapping device in the present embodiment.The urban mapping device is based on vegetation
Index is reduced, the phenomenon of urban lighting data increase, by processing vegetation index and light data, you can obtain target
The city index in city, is charted based on city index, you can obtain the city design sketch of target cities.As shown in figure 3, should
Urban mapping device includes that remote sensing image data acquisition module 10, target vegetation index acquisition module 20, original light data connect
Receive module 30, target light data acquisition module 40, city index acquisition module 50 and urban mapping processing module 60.
Remote sensing image data acquisition module 10, for the remote sensing image of the target cities of receiving user's input, obtains remote sensing
Image data.
Wherein, the remote sensing image can be middle low resolution remote sensing image, because middle low resolution remote sensing image has shadow
As broad covered area and cheap advantage so as to apply and there is broad covered area and cheap in urban mapping,
The cost that charts can effectively be reduced.
In the present embodiment, the landsat7ETM+ of receiving user's input this intermediate-resolution remote sensing image, landsat7ETM
+ remote sensing image has higher spatial resolution (30m), during ensureing to be charted based on landsat7ETM+ remote sensing images,
The precision of the city design sketch of obtained target cities is higher.Wherein, landsat7 is the land spy of U.S.'s transmitting in 1999
Satellite system is surveyed, Enhanced Thematic Mapper Plus (referred to as ETM+, the special drawing equipment of enhancement mode) is equipped with and is set
Standby, solar radiation that ETM+ feelings of passivity answer earth surface reflection and the heat radiation for distributing have the inductor of 8 wave bands, cover infrared
To the different wavelength range of visible ray.ETM+ equipment is higher in the resolution ratio of infrared band, with higher accuracy, so that most
Whole city design sketch has higher precision.Because ETM+ remote sensing image feelings of passivity answer the solar radiation of earth surface reflection and distribute
Heat radiation, based on ETM+ remote sensing images obtain remote sensing image data when, atmospheric correction need to be carried out, to obtain real earth's surface
Reflectivity.It is to be appreciated that remote sensing image data can also be Sentinal-2A images or other images.
Because the urban mapping device is charted based on vegetation index and urban lighting data so that in the remote sensing image
Should include that the remote sensing image data of vegetation index can be affected.In the present embodiment, remote sensing image data includes near infrared band and red
Optical band.Near infrared light (Near Infrared, NIR) wave band is between visible ray (VI S) and mid-infrared light (MIR)
Electromagnetic wave, by ASTM (testing and material tests association in the U.S.) definition electromagnetic wave of the wavelength in the range of 780～2526nm is referred to,
Traditionally again near infrared region is divided into near-infrared shortwave (780～1100nm) and near-infrared long wave (1100～2526nm) two
Region.Near infrared region is the non-visible region that people have found earliest.Red spectral band is the part in visible light wave range, can
The wave-length coverage of optical band is seen between 0.77～0.622 micron, can be used to calculate vegetation index.
Target vegetation index acquisition module 20, for based on remote sensing image data, the target vegetation for obtaining remote sensing image to refer to
Number.
Wherein, target vegetation index is used to recognize the vegetative coverage situation in remote sensing image that target vegetation index to be bigger, its
The vegetation of covering is more, can be based on the vegetative coverage situation that target vegetation index determines target cities.
Specifically, target vegetation index acquisition module 20, for using vegetation index computing formula to remote sensing image data
Processed, obtained the target vegetation index of remote sensing image；
Wherein, vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI be target vegetation index, NIR
For near infrared band, R is red spectral band.(Normalized Difference Vegetation Index, it is poor to normalize for NDVI
Divide vegetation index), for detecting vegetation generation, vegetation coverage and eliminating the fields such as partial radiation error.Wherein, NIR and R point
Do not refer to the reflectance value of near infrared band and red spectral band.
Further, between [- 1,1], negative value represents that covered ground is cloud, water, snow etc. to the value of NDVI, to visible ray
High reflection；0 indicates rock or exposed soil etc., NIR and R approximately equals；On the occasion of, vegetative coverage is indicated, and increase with coverage
And increase.It is to be appreciated that target vegetation index NDVI is a normalization data, its data acquisition makes between [- 1,1]
When must be processed using target vegetation index NDVI, fast convergence rate can accelerate data-handling efficiency.
When being charted using the target vegetation index of single remote sensing image, target vegetation index and the city for ultimately forming
City's design sketch is associated, and because target vegetation index can affect the precision of city design sketch, therefore need to ensure target vegetation index
Accuracy rate.Therefore, in the urban mapping device, target vegetation index acquisition module 20 includes vegetation index sequence construct unit
21st, vegetation index acquiring unit 22 and target vegetation index updating block 23 are synthesized.
Vegetation index sequence construct unit 21, for same pixel at least three remote sensing images using target cities
Target vegetation index, builds the target vegetation index sequence of pixel.
It is to be appreciated that the target cities covered at least three remote sensing images are identical, it is identical in each remote sensing image
Pixel points to same position in target cities.The target of same pixel is planted at least three remote sensing images using target cities
By index, when building the target vegetation index sequence of pixel, the target vegetation index of same pixel is sorted according to time sequencing,
To form the target vegetation index sequence of the pixel.It is to be appreciated that at least three remote sensing images are according to certain time interval
Collection, it is such as annual or monthly.In the present embodiment by taking year as an example, if the target vegetation index of First Year some day of pixel A is
0.32, Second Year target vegetation index on the same day is 0.40, and target vegetation index on the same day is 0.45 within the 3rd year, then the picture
The target vegetation index sequence of first A is { 0.32,0.4,0.45 }.
Synthesis vegetation index acquiring unit 22, for using default target vegetation index sequence of the composition rule to each pixel
Row are synthesized, and obtain the synthesis vegetation index of pixel.
Wherein, default composition rule includes：Obtain the maximum and minimum of a value of the target vegetation index sequence of pixel；If most
Big value is more than first threshold, then using maximum as pixel synthesis vegetation index；If minimum of a value is less than Second Threshold, will most
Synthesis vegetation index of the little value as pixel；If maximum is not more than and/or minimum of a value is not more than Second Threshold, target is planted
By the intermediate value of exponential sequence as pixel synthesis vegetation index.It is to be appreciated that using default composition rule to each pixel
Target vegetation index sequence synthesized, multiple target vegetation indexs in target vegetation index sequence can be converted into one most
The synthesis vegetation index of the vegetative coverage degree of the corresponding target cities of pixel can be embodied.The span of target vegetation index exists
Between [- 1,1], whether it is vegetation area that first threshold is used to limit, and whether it is water area that Second Threshold is used to limit.If mesh
Mark vegetation index is more than first threshold, then the corresponding region of target vegetation index is vegetation area；If target vegetation index is little
In Second Threshold, then the corresponding region of target vegetation index is water area；If target vegetation index is in first threshold and
Between two threshold values, then the corresponding region of target vegetation index is bare area or city.
In the present embodiment, if first threshold is 0.4, Second Threshold is -0.2；If the target vegetation index sequence of pixel A is
{ 0.55,0.58,0.49,0.45,0.50 }, the target vegetation index sequence of pixel B for -0.1, -0.15, -0.2, -0.18, -
0.22 }, the target vegetation index sequence of pixel C is { 0.13,0.11,0.15,0.2,0.22 }.Enter according to the default composition rule
Row synthesis is processed, then the synthesis vegetation index of pixel A is 0.58, and the synthesis vegetation index of pixel B is -0.22, the synthesis of pixel C
Vegetation index is 0.15.
Target vegetation index updating block 23, for the synthesis vegetation index based on each pixel, updates remote sensing image
Target vegetation index.
Each remote sensing image includes multiple pixels, and the synthesis vegetation index of each pixel is updated in remote sensing image accordingly
The target vegetation index of pixel, to update the target vegetation index of remote sensing image, and the target based on remote sensing image after renewal is planted
Urban mapping is carried out by index, makes the precision of city design sketch for ultimately forming higher, can apparent display City Details part.
Original light data receiver module 30, for the original light data of the target cities of receiving user's input.
Because the realization of the urban mapping device is based on as vegetation index is reduced, what urban lighting data increased shows
As, therefore the original light data of receiving user's input target cities is needed, and original light data is processed, to realize city
The purpose of city's drawing.
Target light data acquisition module 40, for original light data is normalized and resampling process, obtain
Target light data.
Original light data is normalized, original light data is limited in preset range, after facilitating
It is continuous to carry out data processing, Data Convergence speed is improved, improve data-handling efficiency.Due to carrying out normalizing to original light data
After change is processed, Pixel size may be caused undesirable, need to make different pixels using identical resolution ratio, therefore need to be carried out
Resampling is processed, to ensure that each pixel of the target light data for getting has identical spatial resolution, so that subsequently
The process convenience of calculation of city index is obtained based on target light data, accelerates convergence rate, improved.
Target light data acquisition module 40 specifically includes normalized unit 41 and resampling processing unit 42.
Normalized unit 41, for being normalized to original light data using linear normalization formula,
Obtain normalization light data.
Wherein, linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalization lamp
Light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
It is to be appreciated that the original light data of the target cities of user input has maximum and minimum of a value, by target
The original light data RNTL of each pixel in city is normalized, to obtain the normalization light data of the pixel
NTL', and then the normalization light data of target cities is obtained, follow-up urban mapping is carried out using the normalization light data, can
Accelerate Data Convergence speed to a certain extent, improve data-handling efficiency.
Resampling processing unit 42, for carrying out resampling to normalizing light data using closest distribution method, to obtain
Take the target light data that there is same spatial resolution with remote sensing image.
Wherein, closest distribution method is, for the resampling technique of discrete (classification) data, will not to change input block
Value.The position of unit center in output grid data set is navigated to after input raster, closest distribution method will determine input grid
Unit center position nearest on lattice simultaneously the value of the unit is distributed to the unit on output grid.In the present embodiment, using most
Neighbouring distribution method carries out resampling to normalizing light data, to obtain the target with remote sensing image with same spatial resolution
Light data, so as to subsequently obtain the process convenience of calculation of city index based on target light data, further speeds up convergence speed
Degree, improves treatment effeciency.
City index acquisition module 50, for based on target vegetation index and target light data, obtaining city index.
Due to existing in remote sensing image as vegetation index is reduced, the phenomenon of urban lighting data increase, based on the phenomenon
Vegetation index and the incidence relation between light data and city index are obtained, the incidence relation is recycled to target vegetation index
Processed with target light data, you can obtain city index, can be charted using the city index.
Specifically, city index acquisition module 50, for using city index construction formula to target vegetation index and institute
State target light data to be processed, to obtain city index.
Wherein, index construction formula in city includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city index,
NTL is target light data, and NDVI is target vegetation index.
It is to be appreciated that target light data be original light data is normalized and resampling process result,
When obtaining city index using target light data, have the advantages that fast convergence rate, treatment effeciency are high.Target vegetation index
NDVI is a normalization data, and its data acquisition is between [- 1,1] so that at target vegetation index NDVI
During reason, fast convergence rate can accelerate data-handling efficiency.
Urban mapping processing module 60, for being charted based on city index, obtains the city design sketch of target cities.
Because city index and target vegetation index and target night lights correlation of indices join so that entered based on city index
The pixel accuracy of city design sketch of the row drawing to be formed is high, can clearly show that City Details part.
As shown in Fig. 2 in the urban mapping device, based on the remote sensing image data of target cities, the target vegetation of acquisition
Index (NDVI) is as shown in A figures in Fig. 2；Original light data is normalized and resampling is processed, the target light of acquisition
Data (NTL) are as shown in B figures in Fig. 2；Based on target vegetation index (NDVI) and target light data (NTL), obtain city and refer to
Number (NDUI) is as shown in C figures in Fig. 2；Charted based on city index, obtained D in the city design sketch such as Fig. 2 of target cities
Shown in figure.
In the urban mapping device that the present embodiment is provided, target vegetation index is obtained based on remote sensing image, obtained original
Light data obtains target light data, recycles target vegetation index and target light data to obtain city index, using city
City's index is charted.In the urban mapping device, processing procedure is simple and convenient, high degree of automation, and treatment effeciency is high, without the need for
Carry out the preprocessing process of complexity；And city index is obtained based on target vegetation index and target light data, using city
Index is charted, and mapping effect is good, high precision, it is easy to promote the use of.
The present invention is illustrated by several specific embodiments, it will be appreciated by those skilled in the art that, without departing from
In the case of the scope of the invention, various conversion and equivalent substitute can also be carried out to the present invention.In addition, for particular condition or tool
Body situation, can make various modifications, without deviating from the scope of the present invention to the present invention.Therefore, the present invention is not limited to disclosed
Specific embodiment, and whole embodiments for falling within the scope of the appended claims should be included.
Claims (10)
1. a kind of urban mapping method, it is characterised in that comprise the steps：
The remote sensing image of the target cities of receiving user's input, obtains remote sensing image data；
Based on the remote sensing image data, the target vegetation index of the remote sensing image is obtained；
The original light data of the target cities of receiving user's input；
The original light data is normalized and resampling is processed, obtain target light data；
Based on the target vegetation index and the target light data, city index is obtained；
Charted based on the city index, obtained the city design sketch of the target cities.
2. urban mapping method according to claim 1, it is characterised in that the remote sensing image data includes near-infrared ripple
Section and red spectral band；
It is described that the target vegetation index of the remote sensing image is obtained based on the remote sensing image data, including：Using vegetation index
Computing formula is processed the remote sensing image data, obtains the target vegetation index of the remote sensing image；
Wherein, the vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI be target vegetation index, NIR
For near infrared band, R is red spectral band.
3. urban mapping method according to claim 2, it is characterised in that described based on the remote sensing image data, obtains
The target vegetation index of the remote sensing image is taken, is also included afterwards：
Using the target vegetation index of same pixel at least three remote sensing images of the target cities, the pixel is built
Target vegetation index sequence；
The target vegetation index sequence of each pixel is synthesized using default composition rule, obtains the conjunction of the pixel
Into vegetation index；
Based on the synthesis vegetation index of each pixel, the target vegetation index of the remote sensing image is updated；
Wherein, the default composition rule includes：Obtain the maximum and minimum of a value of the target vegetation index sequence of the pixel；
If the maximum be more than first threshold, using the maximum as the pixel synthesis vegetation index；If the minimum
Value be less than Second Threshold, then using the minimum of a value as the pixel synthesis vegetation index；If the maximum be not more than and/
Or the minimum of a value is not more than the Second Threshold, then using the intermediate value of the target vegetation index sequence as the pixel conjunction
Into vegetation index.
4. urban mapping method according to claim 3, it is characterised in that described that the original light data is returned
One changes and resampling process, obtains target light data, including：
The original light data is normalized using linear normalization formula, obtains normalization light data；
Resampling is carried out to the normalization light data using closest distribution method, there is phase with the remote sensing image to obtain
The target light data of isospace resolution ratio；
Wherein, the linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalization lamp
Light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
5. urban mapping method according to claim 4, it is characterised in that described based on the target vegetation index and institute
Target light data is stated, city index is obtained, including：
The target vegetation index and the target light data are processed using city index construction formula, to obtain
State city index；
Wherein, the city index construction formula includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city index,
NTL is target light data, and NDVI is target vegetation index.
6. a kind of urban mapping device, it is characterised in that include：
Remote sensing image data acquisition module, for the remote sensing image of the target cities of receiving user's input, obtains remote sensing image number
According to；
Target vegetation index acquisition module, for based on the remote sensing image data, obtaining the target vegetation of the remote sensing image
Index；
Original light data receiver module, for the original light data of the target cities of receiving user's input；
Target light data acquisition module, for the original light data is normalized and resampling process, obtain mesh
Beacon light light data；
City index acquisition module, for based on the target vegetation index and the target light data, obtaining city index；
Urban mapping processing module, for being charted based on the city index, obtains the city effect of the target cities
Figure.
7. urban mapping device according to claim 6, it is characterised in that the remote sensing image data includes near-infrared ripple
Section and red spectral band；
The target vegetation index acquisition module, at using vegetation index computing formula to the remote sensing image data
Reason, obtains the target vegetation index of the remote sensing image；
Wherein, the vegetation index computing formula includes NDVI=(NIR-R)/(NIR+R)；NDVI be target vegetation index, NIR
For near infrared band, R is red spectral band.
8. urban mapping device according to claim 7, it is characterised in that the target vegetation index acquisition module bag
Include：
Vegetation index sequence construct unit, for the mesh of same pixel at least three remote sensing images using the target cities
Mark vegetation index, builds the target vegetation index sequence of the pixel；
Synthesis vegetation index acquiring unit, for using default target vegetation index sequence of the composition rule to each pixel
Synthesized, obtained the synthesis vegetation index of the pixel；
Target vegetation index updating block, for the synthesis vegetation index based on each pixel, updates the remote sensing image
Target vegetation index；
Wherein, the default composition rule includes：Obtain the maximum and minimum of a value of the target vegetation index sequence of the pixel；
If the maximum be more than first threshold, using the maximum as the pixel synthesis vegetation index；If the minimum
Value be less than Second Threshold, then using the minimum of a value as the pixel synthesis vegetation index；If the maximum be not more than and/
Or the minimum of a value is not more than the Second Threshold, then using the intermediate value of the target vegetation index sequence as the pixel conjunction
Into vegetation index.
9. urban mapping device according to claim 8, it is characterised in that the target light data acquisition module bag
Include：
Normalized unit, for being normalized to the original light data using linear normalization formula, is obtained
Take normalization light data；
Resampling processing unit, for carrying out resampling to the normalization light data using closest distribution method, to obtain
With the target light data that the remote sensing image has same spatial resolution；
Wherein, the linear normalization formula includes NTL'=(RNTL-Min)/(Max-Min), wherein, NTL' is normalization lamp
Light data, RNTL is original light data, and Max and Min is respectively the maximum and minimum of a value of original light data.
10. urban mapping device according to claim 9, it is characterised in that the city index acquisition module, for adopting
The target vegetation index and the target light data are processed with city index construction formula, to obtain the city
Index；
Wherein, the city index construction formula includes：NDUI=(NTL-NDVI)/(NTL+NDVI)；NDUI is city index,
NTL is target light data, and NDVI is target vegetation index.
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