CN111861934A - Hyperspectral satellite image data production, mosaic and metadata manufacturing method - Google Patents
Hyperspectral satellite image data production, mosaic and metadata manufacturing method Download PDFInfo
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Abstract
The invention discloses a hyperspectral satellite image data production, mosaic and metadata manufacturing method, which makes full use of a hyperspectral satellite data source and supplements an image vulnerability area existing in the acquisition process by adopting two modes of archived data or programmed shooting which meet the time phase requirement; firstly, performing radiation correction production and atmospheric correction production on a hyperspectral satellite remote sensing image, then finishing geometric fine correction, namely orthorectification, of a single-scene image by combining DEM (digital elevation model) and DOM (document object model) data, performing image mosaic, dodging and evening on a true color image of the orthorectification image and manufacturing mosaic line metadata, and finally obtaining hyperspectral satellite image data with the spatial resolution superior to 10 meters; finally, carrying out quality inspection; the invention has wide coverage, high image resolution and high quality.
Description
Technical Field
The invention belongs to the technical field of remote sensing image processing, and particularly relates to a method for producing and embedding hyperspectral satellite image data and manufacturing metadata.
Background
With the deep implementation of the big data strategy, the contradiction that the remote sensing image data cannot meet the service guarantee in the aspects of annual coverage, image resolution, updating period and the like is increasingly obvious, and the demand for establishing a remote sensing image overall guarantee service system is more urgent. The remote sensing image data resource acquisition method aims to better acquire, manage, serve and apply remote sensing image data resources and improve the service guarantee capability of the remote sensing image.
At present, aiming at the special province of the Guizhou province, the data acquisition plan of the hyperspectral satellite is to use the existing in-orbit satellite (namely 8 currently in-orbit hyperspectral satellites) to carry out transit shooting, and the transit time is set between 12 and 14 hours in Beijing, so that the hyperspectral image data in the Guizhou region can be acquired from twelve noon to two afternoon points every day. Due to the weather reason of the Guizhou area, the satellite remote sensing image is extremely difficult to obtain, so after the obtaining difficulty of the Guizhou area remote sensing image is comprehensively considered, the hyperspectral satellite image is obtained based on the application scene of the hyperspectral satellite image, in order to be more beneficial to the development of the economic society and enable the work of adjusting the agricultural industry structure, uniformly determining the rights of natural resources and the like to be efficiently carried out and accurately carried out, the hyperspectral satellite remote sensing image of the remote sensing image overall planning task is recommended to be obtained, the hyperspectral satellite image can be obtained according to areas such as cities, towns, agricultural planting areas, mining areas, forest areas and the like, and the hyperspectral satellite image overall planning task is completed according to main planting areas covering important economic crops such as corns, tea.
Disclosure of Invention
The invention aims to provide a method for embedding hyperspectral satellite image data and manufacturing metadata, which has wide coverage range and high image resolution.
The technical scheme of the invention is as follows: a hyperspectral satellite image data production, inlay and metadata preparation method, make full use of hyperspectral satellite data source, and adopt the filing data or programming shooting two kinds of ways meeting the time phase requirement to supplement to the image loophole area existing in the acquisition process; performing data quality inspection, metadata normalization, cloud and snow range delineation and warehousing on the acquired hyperspectral satellite remote sensing images, and performing supplementary coverage on cloud and snow coverage areas by using other images in the same period; firstly, performing radiation correction production and atmospheric correction production on a hyperspectral satellite remote sensing image, then finishing geometric fine correction, namely orthorectification, of a single-scene image by combining DEM (digital elevation model) and DOM (document object model) data, performing image mosaic, dodging and evening on a true color image of the orthorectification image and manufacturing mosaic line metadata, and finally obtaining hyperspectral satellite image data with the spatial resolution superior to 10 meters; finally, carrying out quality inspection;
the radiation correction is absolute radiation correction of the hyperspectral satellite remote sensing image, the radiation correction operation is carried out on the spectral reflectivity or spectral radiance of an original image ground object in the production process, and the luminance gray value of the original image is converted into absolute radiance when images acquired at different times are spliced in the implementation process;
the atmospheric correction is to set reasonable atmospheric correction parameters according to conditions such as the geographical position, the altitude and the climate of a target area of the hyperspectral satellite remote sensing image, eliminate the influence of water vapor, aerosol and the like on the reflectivity of the ground object and obtain the real reflectivity of the ground object.
Specifically, the radiation correction formula used by the hyperspectral satellite image is as follows:
in the formula: le is the apparent radiance; gain is the absolute radiation correction gain coefficient; offset is the absolute radiometric correction offset coefficient; TDISTage is an integral progression, and the information of TDISTages fields in the metadata file query fields of the hyperspectral data folder is obtained.
Specifically, the radiation correction converts a digital quantization value of an image into a radiation brightness value or a reflectivity, absolute radiation correction parameters comprise a gain coefficient, an offset coefficient and an integral series, the parameters are stored in a metadata file, and a radiation correction tool in ENVI is used for automatically reading the parameters from the metadata file so as to finish the radiation correction; the three files after radiation correction are respectively raster data in dat format, metadata in xml format and a header file in hdr format, wherein image parameters such as RPC information, time, coordinates and the like of the image are stored in the header file, so that correct parameters are provided for atmospheric correction.
Specifically, the atmospheric correction uses a spatial statistic tool in the ENVI to count the DEM same region of the collected basic mapping 1:10000 and calculate an average value, selects an image with absolute radiation correction completed, and automatically reads the longitude and latitude and the imaging time of an image header file by using the atmospheric correction tool of the ENVI; calculating the values of an Atmospheric Model, Aerosol Retrieval, Water Retrieval and Water Absorption Feature of a target image by using an ENVI extending tool FLAASHSettingguide, selecting parameters of an atmosphere Model according to imaging time and latitude information, and setting other parameters of hyperspectral data so as to finish atmosphere correction; the image input in the FLAASH model must be a radiation brightness image after radiation correction, and in order to perform atmospheric inversion, the image at least comprises 15nm resolution and higher bands in the following three range intervals, namely 1050-; for the hyperspectral remote sensing image of the existing sensor type, the image header file must contain the wavelength and the spectrum bandwidth.
More specifically, the AtmosphericModel is an atmospheric model, is determined according to the image center latitude and the acquisition month, and is completed by means of a help document; aerosol Retrieval is an Aerosol inversion method, a dark pixel reflectance model is used for estimating the content and the average visibility of the image Aerosol, and if Aerosol inversion is carried out, short-wave infrared band support is required; WaterRetrieval is whether to perform water vapor inversion.
Specifically, the ground control points are extracted by using the DOM and DEM data of the reference image, and the error generated by the non-system factor is corrected through a rational function model; registering the hyperspectral satellite remote sensing image and a reference image by using a control point acquisition module, a homonymy point acquisition and optimization module and an orthorectification module, and selecting a control point to correct the hyperspectral satellite remote sensing image on the basis of taking the reference image as a control basis; selecting a rational function model in the acquisition mode of the control points, and uniformly distributing the control points in the whole scene range; the hyperspectral data control point acquisition parameter setting method comprises the steps that a mathematical model is a rational function model, the order is 1, the control point acquisition number setting is determined according to image data processed in batches, a fast Fourier phase transformation matching method is adopted in a registration algorithm, images and DEM data need to be referenced when control points are acquired, and the control points can be automatically acquired after parameter submission operation is set; after the control points are collected, the data can be subjected to orthorectification, and an output file format, projection information, a data space rate and a resampling method can be set; the result after the orthorectification can be checked by being matched with the reference image.
Specifically, the light and color homogenizing treatment specifically comprises the following steps:
step 2, adjusting contrast/color saturation by adopting a filtering and histogram stretching method;
step 3, carrying out uniform light treatment by adopting an ENVI histogram equalization and histogram matching method; wherein, the original hyperspectral satellite image data of the L1B level and the original hyperspectral satellite image data of the L4 level are not subjected to uniform light and color treatment;
and 4, sharpening the true color image by adopting an ENVI software 2% linear stretching method.
Specifically, the image mosaic is mosaic processing by utilizing ENVI or Photoshop, and when an artificial ground object exists in a mosaic area, a mosaic line is manually drawn to bypass the artificial ground object, so that the integrity and the rationality of the mosaic result are kept.
Specifically, the mosaic line element data is made in such a way that the hues at two sides of the joint edge of the hyperspectral true-color image are kept consistent as much as possible, the joint edge between the images is corrected according to the precision of the joint edge, and the corrected joint edge difference does not exceed the precision requirement of 4 pixels; making L1B-grade original hyperspectral satellite image metadata and L4-grade hyperspectral satellite image metadata, and storing in an shp format; mosaic line metadata is made and stored in the shp, xls format.
Specifically, the quality inspection comprises manual comparison inspection, program automatic inspection and human-computer interaction inspection;
the manual comparison inspection comprises the following steps: the correctness of the inspection content is judged by checking the project result through manual inspection and carrying out visual inspection; visually checking the quality of the remote sensing image in ArcGIS and ENVI software, and checking whether the image has noise, abnormal values, stripes, complete wave band information and the like; recording the image with problems in a problem area vector mode so as to replace the image at a later period;
the program automatically checks: checking the coordinate system, the edge connection, the grid quality, the cutting range and other checking items of the project result by using automatic quality inspection software, and checking and finding errors in the data;
the human-computer interaction check comprises the following steps: acquiring a check point by using 1:50000 geographical national situation census DOM control data, and calculating a point position error and a median error of the check point to evaluate the plane precision of the single scene correction image; the error in the point location of the digital ortho-image ground object point relative to the checking point must not exceed the specified accuracy requirement of mountainous and high mountain lands, and the point with the point location error exceeding 60 meters needs to be further checked and verified.
Compared with the prior art, the invention has the beneficial effects that: the invention applies remote sensing image data resources and improves the service guarantee capability of remote sensing images, and can manufacture products with wide coverage range and high image resolution, including L1B-grade high-spectrum satellite image data, L4-grade high-spectrum satellite image result data and high-spectrum true-color mosaic images.
Drawings
FIG. 1 is a technical roadmap for a particular fabrication method of the present invention;
FIG. 2 is a schematic diagram of a technical route for producing L4 grade high-spectrum satellite images;
FIG. 3 is a naming rule diagram of L1B class original hyperspectral satellite image data;
FIG. 4 is a comparison of pre-radiation correction and post-radiation correction;
FIG. 5 is a comparison of pre-atmospheric correction and post-radiation correction;
FIG. 6 is a comparison graph of the before and after atmospheric correction spectra;
FIG. 7 is a high spectral true color mosaic product manufacturing technology roadmap.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-7, a hyperspectral satellite image data production, mosaic and metadata manufacturing method fully utilizes a hyperspectral satellite data source, and supplements an image vulnerability area existing in an acquisition process by adopting two modes of archived data or programmed shooting which meets time phase requirements; performing data quality inspection, metadata normalization, cloud and snow range delineation and warehousing on the acquired hyperspectral satellite remote sensing images, and performing supplementary coverage on cloud and snow coverage areas by using other images in the same period; firstly, performing radiation correction production and atmospheric correction production on a hyperspectral satellite remote sensing image, then finishing geometric fine correction, namely orthorectification, of a single-scene image by combining DEM (digital elevation model) and DOM (document object model) data, performing image mosaic, dodging and evening on a true color image of the orthorectification image and manufacturing mosaic line metadata, and finally obtaining hyperspectral satellite image data with the spatial resolution superior to 10 meters; finally, carrying out quality inspection;
the radiation correction formula used by the hyperspectral satellite image is as follows:
in the formula: le is the apparent radiance; gain is the absolute radiation correction gain coefficient; offset is the absolute radiometric correction offset coefficient; TDISTage is an integral progression, and the information of TDISTages fields in the metadata file query fields of the hyperspectral data folder is obtained.
The radiation correction is absolute radiation correction of the hyperspectral satellite remote sensing image, absolute radiation correction is carried out on the hyperspectral satellite remote sensing image by utilizing radiation correction coefficients (gain parameters and offset parameters) attached to the original hyperspectral satellite remote sensing image, radiation correction operation is carried out on the spectral reflectivity or spectral radiation brightness of an original image ground object in the production process, and when images acquired at different times are spliced in the implementation process, the brightness gray value of the original image is converted into absolute radiation brightness;
and the radiation correction converts the digital quantization value of the image into a radiation brightness value or a reflectivity, absolute radiation correction parameters comprise a gain coefficient, an offset coefficient and an integral series, the parameters are stored in a metadata file, and the radiation correction tool in ENVI is used for automatically reading the parameters from the metadata file, so that the radiation correction is completed.
Performing absolute radiation correction on 32 wave bands by adopting ENVI software based on an L1B-level original hyperspectral satellite image, converting a digital quantization value (DN) of the image into a radiance value or a reflectivity, wherein absolute radiation correction parameters comprise a gain coefficient, an offset coefficient and an integral series, the parameters are stored in a metadata file of \ "XXX + _ meta.xml" of the L1B-level original hyperspectral satellite image, and a radiation correction tool (Radiometric correction) in the ENVI is used for automatically reading the parameters from the metadata file, so that the radiation correction is completed, and the corrected three files are as follows: the raster data in dat format, the metadata in xml format, and the header file in hdr format (where the image parameters such as RPC information, time, coordinates, etc. of the image are stored in the header file to provide correct parameters for atmospheric correction).
The atmospheric correction is to set reasonable atmospheric correction parameters according to conditions such as the geographical position, the altitude and the climate of a target area of the hyperspectral satellite remote sensing image, eliminate the influence of water vapor, aerosol and the like on the reflectivity of the ground object and obtain the real reflectivity of the ground object.
Solar radiation is incident on the surface of an object through the atmosphere in some way and then reflected back to the sensor, and due to images of atmospheric aerosol, terrain, nearby terrain and the like, the original image contains the integration of information such as information of the surface of the object, the atmosphere and the sun. If one wants to know the spectral properties of the surface of an object, one has to separate its reflection information from the information of the atmosphere and the sun, which requires an atmosphere correction process.
And (3) utilizing a computer Statistics tool in the ENVI to count the collected and covered basic surveying and mapping 1:10000 digital elevation model result (DEM) in the same area and calculate an average value (Ground migration), selecting an image with absolute radiation correction completed, and utilizing an atmosphere correction tool (FLAASH Atmosphericcorrection) of ENVI software to automatically read the longitude and latitude and the imaging time of an image head file. Calculating the values of an Atmospheric Model, an Areosol Retrieval, a Water Retrieval and a WaterAbsorption Feature of the target image by using an ENVI expanding tool FLAASH Settingguide, selecting parameters of an atmosphere Model according to imaging time and latitude information, and setting other parameters of hyperspectral data so as to finish atmosphere correction.
The input image in the FLAASH model must be a radiance image after radiation correction, the format is BIL, and in order to perform atmospheric inversion, the image at least comprises 15nm resolution or even higher wavelength bands in the following three range intervals, namely 1050-. For the existing sensor type of hyperspectral remote sensing image, the image header file must contain the wavelength and the spectral bandwidth (FWHM).
Setting model parameters:
the altitude is the average altitude of the single-scene image coverage area.
And (3) data acquisition date and satellite transit time, wherein the satellite transit time is Greenwich mean time and is automatically acquired from the image header file after absolute radiation correction is completed.
Input radiation Image: selecting the result after the previous step of radiometric calibration;
radiance Scale Factors: selecting a Use single scale factor for all bands, wherein the numerical value keeps default 1, the unit of an original radiation calibration result is W.m < -2 > sr < -1 > mum < -1 >, the unit of input radiation brightness data required by FLAASH is μ W.cm < -2 > sr < -1 > nm < -1 >, the difference between the units is just 10 times, and the conversion of the scale factor unit is already carried out during radiation calibration, so that the default 1 is kept;
output reflection File: setting an earth surface reflectivity data output path and a file name (which must be an English digital path and a name) after atmospheric correction;
output Directory for FLAASH Files: generating storage paths of other files in the correction process, defaulting to a temporary folder of a current user system, if the folder has no authority or the space of a disk where the folder is located is insufficient, suggesting to modify the folder to other disks, otherwise, generating an error with a code of 102;
rootname for FLAASH Files: outputting file name prefixes, wherein the file name prefixes can be not filled;
scene Center Location: automatically reading the longitude and latitude of the image center from a result header file after absolute radiometric calibration;
sensor Type: sensor type, here NKNOWN-HSI;
sensor Altitude (km): sensor height, 520 km;
group Elevation (km): acquiring the average elevation of the ground of a region corresponding to the image through a DEM (digital elevation model), wherein the attention unit is km;
pixel Size (m): pixel size, 10 m;
flight Date: and image acquisition time, namely automatically acquiring, wherein Greenwich mean time needs to be input.
Atmospheric Model: the atmospheric model is generally determined according to the image center latitude and the acquisition month, and is completed by help of a help document. Here, Tropical is selected;
water Retrieval: if the Water vapor inversion is not carried out, Yes is selected, the lower Water AbsorptionFiture option is activated at the moment, 1135/940/820nm three options are selected, and 940 is selected here;
aerosol Model: the aerosol model has four options of Rural, Urban, Maritime and Tropospheric. Observing the image can find that 50% of the image is covered by the urban and industrial areas;
aerosol Retrieval: the aerosol inversion method estimates the content and the average visibility of the image aerosol by using a dark pixel reflectance model, and three options of non, 2-Band (K-T) and 2-Band Over Water are selectable. If aerosol inversion is carried out, short wave infrared band support is needed, and None is selected here;
initial Visibility (km): the initial visibility is set according to the atmospheric condition when the image is obtained, if the aerosol cannot be inverted, the value is used as an initial value to participate in atmospheric correction, and the default value is kept;
spectral Polishing: smoothing the spectrum, keeping default Yes;
width (number of bands): spectral smoothing window size. The larger the number, the smoother the output reflectance data spectrum, and the more computationally efficient the odd values than the even values, where default is maintained.
Wavelet Recalibration: the input wavelength calibration, AVIRIS, HYDICE, HyMap, HYPERION, CASI and AISA sensor ENVI will calibrate automatically, and the other hyperspectral sensors will need to provide an additional spectrometer definition file, here holding the default No.
Hyperspectral Settings: clicking Hyperspectral Settings from the lower part, and opening a Hyperspectral parameter setting panel; inverting the wave band of the water vapor channel, and automatically finding the corresponding wave band by default.
Advanced settings: clicking Advanced settings, and opening an Advanced parameter setting panel; no, it is not recommended to choose Yes, because there are a lot of 0 values in the first or last columns of each band of the image, which may bring about errors;
the FLAASH atmospheric correction has the advantage that the atmospheric aerosol influence is eliminated by calculating the average elevation value of each area, so that the atmospheric correction processing can be performed by respectively performing targeted selection correction models on different terrains and different areas.
The orthorectification utilizes the DOM and DEM data of the reference image to extract ground control points, and corrects errors generated by non-system factors through a rational function model:
1) the hyperspectral satellite remote sensing image is registered with a reference image by utilizing a control point acquisition, homonymy point acquisition and optimization and orthorectification module in GXL software, and the control point is selected to correct the hyperspectral satellite remote sensing image on the basis of the reference image. And selecting a rational function model for the acquisition mode of the control points, and uniformly distributing the control points in the whole scene range.
The parameter setting of hyperspectral data control point acquisition is carried out, a rational function model is selected as a mathematical model, the order is selected to be 1, the control point acquisition number setting is determined according to image data processed in batches, a fast Fourier phase transformation matching method is adopted in a registration algorithm, images and DEM data need to be referenced when the control points are acquired, and the control points can be automatically acquired after parameter submission operation is set.
After the control points are collected, the data can be subjected to orthorectification, and an output file format, projection information, a data space rate and a resampling method can be set. The result after the orthorectification can be checked by being matched with the reference image.
The registration precision between two images is not more than 8 pixels (on a hyperspectral satellite image), and the typical ground features and terrain features (such as valleys and ridges) cannot have double images. If the requirement of registration accuracy is not met, GXL is put in to reset parameters of control point acquisition and orthorectification for orthorectification.
The light and color homogenizing treatment comprises the following specific steps:
1) and performing image enhancement processing on the hyperspectral true-color image according to the requirement.
2) And performing contrast/color saturation adjustment by adopting a filtering and histogram stretching method.
3) And (4) carrying out dodging processing by adopting an ENVI software histogram equalization and histogram matching method. Wherein, the original hyperspectral satellite image data of the L1B level and the original hyperspectral satellite image data of the L4 level are not subjected to uniform light and color treatment.
4) And sharpening the true color image by adopting an ENVI software 2% linear stretching method.
The image mosaic is mosaic processing by utilizing ENVI or Photoshop, and when an artificial ground object exists in a mosaic area, a mosaic line is manually drawn to bypass the artificial ground object, so that the mosaic result keeps the integrity and rationality of the artificial ground object.
The mosaic line metadata is manufactured in such a way that the hues at two sides of the joint edge of the hyperspectral true-color image are kept consistent as much as possible, the joint edge between the images is corrected according to the precision condition of the joint edge, and the corrected joint edge difference does not exceed the precision requirement of 4 pixels; making L1B-grade original hyperspectral satellite image metadata and L4-grade hyperspectral satellite image metadata, and storing in an shp format; mosaic line metadata is made and stored in the shp, xls format.
Table 1 product grade description
As shown in table 1, the hyperspectral satellite image data result includes L1B-level original hyperspectral satellite image data of the whole scene subjected to relative radiation correction, L4-level hyperspectral satellite image data of the whole scene subjected to absolute radiation correction, atmospheric correction and orthorectification, and hyperspectral true-color mosaic image data.
L1B-level original hyperspectral satellite image data
The original hyperspectral satellite image data consists of eleven parts, namely image data files (32 files), quick views (32 files), positioning model files (32 files), metadata files, thumb maps, vector files (shp and shx formats), projection information files (dbf and prj formats), push charts and geometry and radiation quality inspection files.
(1) The L1B-grade original hyperspectral satellite image data are stored by adopting an original uncompressed folder format, parameters and a naming mode, and the image format is GeoTIFF.
(2) And storing the L1B grade original hyperspectral satellite image fast view in a naming mode, wherein the format of the fast view is JPEG (jpg).
(3) An L1B-grade original hyperspectral satellite image positioning model file (RPC) is stored in a naming mode, and the file format is a text format ([ word _ rpc.txt).
(4) The L1B-grade original hyperspectral satellite image metadata file is stored in a naming mode, and the file format is XML format ([ meta ] meta.
(5) The L1B grade original hyperspectral satellite image thumb map is stored by adopting the naming mode, and the format of the thumb map is JPEG (JPEG).
(6) And storing the L1B-grade original hyperspectral satellite image vector file in a name mode, wherein the vector file format is an SHP format ([ SHP ]).
(7) And storing the L1B-grade original hyperspectral satellite image vector file in a name mode, wherein the format of the vector file is SHX (. SHX).
(8) And storing the L1B-grade original hyperspectral satellite image projection information file in a naming mode, wherein the projection information file is in a DBF (star. DBF) format.
(9) The L1B-grade original hyperspectral satellite image projection information file is stored in a naming mode, and the projection information file is in a PRJ (star PRJ) format.
(10) The pushed data mapping table file is stored in an SHP format.
(11) The L1B-grade original hyperspectral satellite image geometry and radiation quality inspection file is in an XML format (x _ qual.xml).
The L1B-level original hyperspectral satellite image data is stored by taking a single scene as a basic unit, and comprises 32 wave band image data, 32 wave band corresponding fast views (32), 32 wave band positioning model files (RPC), a metadata file, a thumb chart, 2 vector files, 2 projection information files, 1 pushed data graph file and a geometric and radiation quality inspection file.
The L1B level original hyperspectral satellite image contains image data of 32 wave bands, the number and naming of fast view and positioning model files (RPC) are in one-to-one correspondence with the original images of the 32 wave bands, 15 wave bands of the scene image are adopted as naming rules for a projection information file, a vector file and a thumb map, all the information of the geometry, a radiation quality inspection file and a metadata file contains all the wave band information of the scene image, so that the naming is carried out by removing the wave band information of the scene image, and the image name below the visual context of a document is replaced by XXX (original image name), for example: XXX.
L4 grade high spectrum satellite image data
The L4-level high-spectrum satellite image data consists of three parts, namely a digital orthoimage file, a precision check information file and a metadata file.
(1) The digital ortho images are stored in an uncompressed IMG format.
(2) The single scene precision check point file is stored in an SHP format.
(3) The single scene accuracy check list file is stored in the EXCEL (. xls) format.
(4) And storing the edge splicing precision check point file in an SHP format.
(5) The edge precision check table file is stored in an EXCEL (x.xls) format.
(6) The metadata is stored in SHP format.
High-spectrum true-color mosaic image
The high-spectrum true-color mosaic image comprises intra-provincial mosaic image data and a mosaic line file.
(1) The hyperspectral true color intra-province mosaic is stored in an Image (img) format.
(2) The mosaic line file is stored in SHP format.
(3) The mosaic wire file is stored in EXCEL (·. xls) format.
L4 grade high spectrum satellite image data
A storage unit: the L4 level whole scene hyperspectral satellite image result is stored by taking a single scene as a basic unit, wherein the single scene refers to an orthorectified image data file, an accuracy check file and a metadata file which comprise 32 integrated wave bands.
File naming: the main file naming rule of the whole scene L4 level high spectrum satellite image result data file and the corresponding metadata file is as follows: the naming of the L4 level high-spectrum satellite imagery data file is extended _ dq _ ORTHO _ MS after the naming of the corresponding L1B level raw data folder.
High-spectrum true-color mosaic image
The format of a hyperspectral true-color mosaic image data file is img; the file name of the mosaic line metadata is stored in an shp format; the mosaic metadata file is stored in two parts in shp and xls formats respectively;
mosaic line metadata: including. shp files and. xls files; high spectrum true color inlaying and cutting range: after the hyperspectral digital ortho-image with the spatial resolution being better than 10 meters is embedded, according to the administrative boundary of 2000 coordinates; the cutting range of the hyperspectral digital ortho-image data is a rectangle of which the minimum external rectangle of the corresponding basic storage unit extends 100 pixels outwards.
The spatial resolution of L1B-grade original hyperspectral satellite image data is 10 meters, and the spatial resolution of L4-grade hyperspectral satellite image data is 10 meters. The matching precision among 32 wave bands of the L1B-level original hyperspectral satellite image is controlled within 1.5 pixels; the central wavelength of each wave band of the L1B-level original hyperspectral satellite image should float in innumerable values, and if the central wavelength has a floating value, the spectral resolution cannot be exceeded, namely 2.5 nm. The spectrum information of the L1B-level original hyperspectral satellite image should not have a severe distortion phenomenon, the mean value and the variance of the gray values of the same type ground object in the L1B-level original hyperspectral satellite image and a standard spectrum curve should not have large difference, and the trend of the ground object spectrum curve is approximately the same as that of the standard spectrum curve, and the characteristics are obvious. The spectrum information of the L1B-grade original hyperspectral satellite image should not have large-area noise and strips, and the aggregation area of null anomaly points cannot be higher than 2500m2I.e., 5x5 pel size.
When the high-spectrum true color image is connected, the connecting positions of two adjacent scene images are in natural transition, and the contour of the ground object is not seriously staggered or distorted;
level L1B raw image quality:
1) the L1B level original image should be free of large area noise and banding.
2) The registration between the bands of the L1B original image 32 should meet the accuracy requirement.
3) The center wavelength of the original image band of level L1B should not have numerical fluctuations.
4) The spectral information of the ground object of the L1B level image should not be seriously distorted.
L4-grade high-spectrum satellite image quality:
1) the spectral information of the ground object of the L4-grade high-spectrum satellite image should not be seriously distorted.
2) The L4 grade high spectrum satellite image should not have large area noise, banding and flower phenomenon.
3) The spectral information of the ground object of the L4 level image after radiation correction and atmospheric correction should not have characteristic value loss.
4) The L4-level high-spectrum satellite image orthorectification needs to meet the precision requirement.
L1B-level original hyperspectral satellite image data metadata
1) And storing the original hyperspectral satellite image data metadata in a vector (. shp) format according to a standard.
2) The L4-level high-spectrum satellite ortho-image result metadata are stored in a vector (shp) format according to the standard, and the file name is the same as the name of an ortho-corrected image result.
3) The mosaic line result metadata are stored in two formats, namely an EXCEL (x. xls) format and a vector (x. shp) format, and the file name is the same as that of the mosaic image result.
Quality inspection: the method comprises manual comparison inspection, program automatic inspection and human-computer interaction inspection;
manual comparison and inspection: the correctness of the inspection content is judged by checking the project result through manual inspection and carrying out visual inspection; visually checking the quality of the remote sensing image in ArcGIS and ENVI software, and checking whether the image has noise, abnormal values, stripes, complete wave band information and the like; recording the image with problems in a problem area vector mode so as to replace the image at a later period;
automatic checking of a program: checking the coordinate system, the edge connection, the grid quality, the cutting range and other checking items of the project result by using automatic quality inspection software, and checking and finding errors in the data;
human-computer interaction inspection: acquiring a check point by using 1:50000 geographical national situation census DOM control data, and calculating a point position error and a median error of the check point to evaluate the plane precision of the single scene correction image; the error in the point location of the digital ortho-image ground object point relative to the checking point must not exceed the specified accuracy requirement of mountainous and high mountain lands, and the point with the point location error exceeding 60 meters needs to be further checked and verified.
The quality inspection comprises the step of performing quality control on image element data of the original hyperspectral satellite for manufacturing the L1B-level image, and the specific inspection comprises the following steps:
(1) band matching precision: checking whether the matching precision between the L1B-grade original hyperspectral satellite image data wave bands meets the control requirement range;
(2) center wavelength: checking whether satellite ephemeris parameters of L1B-grade original hyperspectral satellite image data are complete or not and whether the central wavelength has numerical value floating or not;
(3) fidelity of spectral information: checking whether the fidelity of spectrum information of the L1B-grade original hyperspectral satellite image data meets the specified requirements, and whether indexes such as standard deviation and the like meet the standards;
(4) null outliers, bands: checking whether null abnormal points and strip areas of L1B-level original hyperspectral satellite image data meet the specified requirements, wherein the null abnormal points and the strip areas cannot exceed 2% of the effective area of a single scene;
(5) image data presence: whether the timeliness of the L1B-grade original hyperspectral satellite image data meets the project regulation requirements is checked.
Process quality control
The production of data product is carried out overall process quality control, and the operating personnel need carry out quality inspection to each intermediate link of production process, can carry out next process operation after the inspection is qualified, must not have to omit and the mistake exists, and process quality control mainly includes:
(1) checking whether the distribution and precision of the DOM data meet the requirement of correcting the ortho-image of the corresponding area.
(2) Checking whether the precision of the collected topographic Data (DEM) and the grid size meet the requirement of correcting the orthoimage of the corresponding area.
(3) And checking whether the situation and the quality condition of the hyperspectral satellite image original data meet the project regulation requirements.
(4) And checking whether the intermediate process control point information meets the error precision requirement.
L4 level resulting image quality control
(1) Quality of radiation correction
Checking whether the radiation correction quality of the L4 grade high-spectrum satellite image data meets the project regulation requirements.
(2) Atmosphere correction mass
Checking whether the atmospheric correction quality of the L4-grade high-spectrum satellite image data meets the project regulation requirements.
Accuracy of orthorectification
And checking whether the mean error, the maximum error and the error distribution in the hyperspectral satellite image ortho-correction precision report are reasonable or not, whether a system error exists or not and whether the residual error in the adjustment report meets the precision requirement or not. Checking whether the geodetic datum, the elevation datum and the projection parameter of the spatial reference system meet the technical requirements or not; checking whether the error in the plane position of the L4 level finished image meets the precision requirement and whether special conditions are recorded in metadata and technical summary; whether the whole-scene ortho-image registration error meets the precision requirement or not; checking whether the storage, organization, file format and file name of the data file meet the requirements or not, and whether the situations of file deletion, redundancy and data reading incapability exist or not; checking whether the image situation meets the requirement, overlapping multiple images in the same area and different time phases, and judging whether the image in the overlapping area meets the requirement; checking whether the image range, the ground resolution and the color characteristics meet the requirements or not, and whether unreasonable image noise, information loss and other conditions exist or not; the content, integrity, data format, data structure, etc. of the metadata are checked for compliance with requirements and the data item is intact.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention.
Claims (10)
1. A method for producing, inlaying and making metadata of hyperspectral satellite image data is characterized by comprising the following steps: the hyperspectral satellite data source is fully utilized, and an image leak area existing in the acquisition process is supplemented by adopting two modes of archived data or programmed shooting which meet the time phase requirement; performing data quality inspection, metadata normalization, cloud and snow range delineation and warehousing on the acquired hyperspectral satellite remote sensing images, and performing supplementary coverage on cloud and snow coverage areas by using other images in the same period; firstly, performing radiation correction production and atmospheric correction production on a hyperspectral satellite remote sensing image, then finishing geometric fine correction, namely orthorectification, of a single-scene image by combining DEM (digital elevation model) and DOM (document object model) data, performing image mosaic, dodging and evening on a true color image of the orthorectification image and manufacturing mosaic line metadata, and finally obtaining hyperspectral satellite image data with the spatial resolution superior to 10 meters; finally, carrying out quality inspection;
the radiation correction is absolute radiation correction of the hyperspectral satellite remote sensing image, the radiation correction operation is carried out on the spectral reflectivity or spectral radiance of an original image ground object in the production process, and the luminance gray value of the original image is converted into absolute radiance when images acquired at different times are spliced in the implementation process;
the atmospheric correction is to set reasonable atmospheric correction parameters according to conditions such as the geographical position, the altitude and the climate of a target area of the hyperspectral satellite remote sensing image, eliminate the influence of water vapor, aerosol and the like on the reflectivity of the ground object and obtain the real reflectivity of the ground object.
2. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the radiation correction formula used by the hyperspectral satellite image is as follows:
in the formula: le is the apparent radiance; gain is the absolute radiation correction gain coefficient; offset is the absolute radiometric correction offset coefficient; TDISTage is an integral progression, and the information of TDISTages fields in the metadata file query fields of the hyperspectral data folder is obtained.
3. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the radiation correction converts the digital quantization value of the image into a radiation brightness value or a reflectivity, absolute radiation correction parameters comprise a gain coefficient, an offset coefficient and an integral progression, the parameters are stored in a metadata file, and the radiation correction tool in ENVI is used for automatically reading the parameters from the metadata file so as to finish the radiation correction; the three files after radiation correction are respectively raster data in dat format, metadata in xml format and a header file in hdr format, wherein image parameters such as RPC information, time, coordinates and the like of the image are stored in the header file, so that correct parameters are provided for atmospheric correction.
4. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the atmospheric correction utilizes a spatial statistic tool in ENVI to count DEM same areas of collected basic mapping 1:10000 and calculate an average value, selects an image with absolute radiation correction completed, and utilizes the atmospheric correction tool of ENVI to automatically read the longitude and latitude and the imaging time of an image header file; calculating the values of an Atmospheric Model, Aerosol Retrieval, Water Retrieval and Water AbsorptionFature of target images by using an ENVI expanding tool FLAASH Setting Guide, selecting Atmospheric Model parameters according to imaging time and latitude information, and Setting other parameters of hyperspectral data so as to finish Atmospheric correction; the image input in the FLAASH model must be a radiation brightness image after radiation correction, and in order to perform atmospheric inversion, the image at least comprises 15nm resolution and higher bands in the following three range intervals, namely 1050-; for the hyperspectral remote sensing image of the existing sensor type, the image header file must contain the wavelength and the spectrum bandwidth.
5. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 4, wherein: the Atmospheric Model is an Atmospheric Model, is determined according to the image central latitude and the acquisition month and is completed by help of a help document; aerosol Retrieval is an Aerosol inversion method, a dark pixel reflectance model is used for estimating the content and the average visibility of the image Aerosol, and if Aerosol inversion is carried out, short-wave infrared band support is required; the Water Retireval is whether to perform Water vapor inversion or not.
6. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the orthorectification utilizes the DOM and DEM data of the reference image to extract ground control points, and corrects errors generated by non-system factors through a rational function model; registering the hyperspectral satellite remote sensing image and a reference image by using a control point acquisition module, a homonymy point acquisition and optimization module and an orthorectification module, and selecting a control point to correct the hyperspectral satellite remote sensing image on the basis of taking the reference image as a control basis; selecting a rational function model in the acquisition mode of the control points, and uniformly distributing the control points in the whole scene range; the hyperspectral data control point acquisition parameter setting method comprises the steps that a mathematical model is a rational function model, the order is 1, the control point acquisition number setting is determined according to image data processed in batches, a fast Fourier phase transformation matching method is adopted in a registration algorithm, images and DEM data need to be referenced when control points are acquired, and the control points can be automatically acquired after parameter submission operation is set; after the control points are collected, the data can be subjected to orthorectification, and an output file format, projection information, a data space rate and a resampling method can be set; the result after the orthorectification can be checked by being matched with the reference image.
7. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the light and color homogenizing treatment comprises the following specific steps:
step 1, performing image enhancement processing on a hyperspectral true color image according to needs;
step 2, adjusting contrast/color saturation by adopting a filtering and histogram stretching method;
step 3, carrying out uniform light treatment by adopting an ENVI histogram equalization and histogram matching method; wherein, the original hyperspectral satellite image data of the L1B level and the original hyperspectral satellite image data of the L4 level are not subjected to uniform light and color treatment;
and 4, sharpening the true color image by adopting an ENVI software 2% linear stretching method.
8. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the image mosaic is mosaic by utilizing ENVI or Photoshop, and when an artificial ground object exists in a mosaic area, a mosaic line is manually drawn to bypass the artificial ground object, so that the mosaic result keeps the integrity and rationality of the artificial ground object.
9. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the method comprises the following steps of preparing mosaic line metadata, wherein the manufacturing mosaic line metadata refers to that the hues at two sides of a connecting edge of a hyperspectral true-color image are kept consistent as much as possible, the connecting edge between the images is corrected according to the connecting edge precision condition, and the corrected connecting edge difference does not exceed the precision requirement of 4 pixels; making L1B-grade original hyperspectral satellite image metadata and L4-grade hyperspectral satellite image metadata, and storing in an shp format; mosaic line metadata is made and stored in the shp, xls format.
10. The hyperspectral satellite image data production, mosaic and metadata production method according to claim 1, wherein: the quality inspection comprises manual comparison inspection, program automatic inspection and human-computer interaction inspection;
the manual comparison inspection comprises the following steps: the correctness of the inspection content is judged by checking the project result through manual inspection and carrying out visual inspection; visually checking the quality of the remote sensing image in ArcGIS and ENVI software, and checking whether the image has noise, abnormal values, stripes, complete wave band information and the like; recording the image with problems in a problem area vector mode so as to replace the image at a later period;
the program automatically checks: checking the coordinate system, the edge connection, the grid quality, the cutting range and other checking items of the project result by using automatic quality inspection software, and checking and finding errors in the data;
the human-computer interaction check comprises the following steps: acquiring a check point by using 1:50000 geographical national situation census DOM control data, and calculating a point position error and a median error of the check point to evaluate the plane precision of the single scene correction image; the error in the point location of the digital ortho-image ground object point relative to the checking point must not exceed the specified accuracy requirement of mountainous and high mountain lands, and the point with the point location error exceeding 60 meters needs to be further checked and verified.
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