369 results about "Atmospheric correction" patented technology

The invention discloses a practical atmospheric correction method for remote sensing images, which comprises three steps: a first step, inverting the optical thickness of atmospheric aerosol from MODIS data by means of a 6S model lookup table based on a dense vegetation algorithm; a second step, calculating total content of atmospheric vapor by using an MODIS data band ratio method, and then calculating the optical thickness of the atmospheric vapor; and a third step, combining the inverted optical thickness of the atmospheric aerosol, the optical thickness of the atmospheric vapor and a DOS model to invert the earth surface reflectance. The calculated earth surface reflectance has high precision; the method has strong practicability, and is applicable to various land satellite images; and the MODIS data can be acquired freely every day; therefore, the method can be used as a service atmospheric correction method for the remote sensing images.

The invention discloses a monitoring device and a method for crop growth uniformity degree, aiming at the problem of low efficiency for manually investigating data. The device comprises a remote sensing image processing module, a plot vector data processing module, a vegetation index processing module and a growth uniformity processing module. The method comprises: obtaining the remote sensing images of a satellite; carrying out radiometric ratification, atmospheric correction and geometric correction according to the remote sensing images of the satellite; classifying crops in the remote sensing images of a satellite to obtain a spatial distribution map; converting a grid classification result in the spatial distribution map into facet vector data; processing the spatial distribution map to correct the plot boundary of the crops; according to the spectral characteristics of the plot in the remote sensing image, calculating the vegetation index of the plot; and calculating the growth uniformity degree index according to the vegetation index of each plot. The invention uses the integration of the grid and the vector data to monitor the growth uniformity degree of crops in natural plots and performs the advantages of remote sensing data.

The invention discloses an environmental satellite 1-based surface temperature single-window inversion method. The method comprises the following steps of: 1, acquiring remote sensing data of an environmental satellite 1B, namely an HJ-1B satellite, of the surface of a region to be tested, preprocessing the remote sensing data to acquire image data and converting a pixel brightness digital number (DN) value of the image data into radiance; 2, performing atmospheric correction on the image data by using an image-based continental offshore stratigraphic test (COST) atmospheric correction model; 3, estimating emissivity epsilon of the surface of the region to be tested by a normalized difference vegetation index (NDVI) threshold method; and 4, inverting the surface temperature by a single-window algorithm according to calculation results obtained in the steps 1 to 3. In the method, the image-based COST model atmospheric correction method and the surface temperature single-window algorithm with no need of an atmospheric water vapor content parameter are applied to the HJ-1B satellite remote sensing data, so that the inversion of the surface temperature is realized.

The invention provides a remote sensed estimating method of the large area planting area and the output, which establishes a spectrum which has the same growing stage and is correspondent with the field crown layer at the base of doing normal radiation and atmosphere adjustment to the remote sensed image, and computes the distance threshold value of the spectrum and compares the distance threshold value with the image initial wave spectrum and the reference wave spectrum to obtain TM plant unit, it then statistics the quantity of the TM plant unit from each image unit of middle resolution ratio imaging spectrum data MODIS with the same period and region to ascertain the planting number of the MODIS image, it then ascertains the planting area of the MODIS image according to each image unit area of the planting number and the MODIS.

A method for inverting optical thickness of boundary layer aerosol includes picking up boundary layer aerosol optical thickness data from atmosphere transmission spectrum by utilizing transmission and radiation principle of atmosphere, obtaining expression reflectivity by carrying out reinterpolation for atmosphere and aerosol modes in boundary layer, thus realizing inversion of calculation for boundary layer aerosol optical thickness . The atmospheric calibration can be carried out based on result calculated out by above method.

The invention relates to a method and a device for calculating the reflectivity of an earth surface, which are used for HJ-1A/B satellites. The method comprises the following steps of: 1) acquiring the optical thickness of atmospheric aerosol by using middle-infrared bands of an HJ-1B charge coupled device (CCD) and an infrared camera on the basis of a dark target method, and acquiring the optical thickness of the atmospheric aerosol by using an HJ-1A large-width quick revisit characteristic on the basis of an invariant target method; 2) calculating a solar zenith angle and a solar azimuth angle and observing the zenith angle and the azimuth angle on the basis of an HJ-1A/binary extensible markup language (BXML) file and image data; 3) simulating radiance Lm on a star through moderate resolution atmospheric transmission (MODTRAN) on the basis of parameters acquired in the steps 1) and 2); 4) establishing an earth surface reflectivity lookup table through the step 3); and 5) calibrating the atmosphere by using the lookup table according to an atmospheric parameter and an image to be calibrated. By the method and the device, the absorbing and scattering performance of the atmosphere on a remote sensing image can be effectively eliminated, the reflectivity of an earth surface target can be recovered, the bottleneck of industry application is eliminated, and the application range of environment disaster reduction moonlet data is further expanded.

The invention discloses a calculation method for large spatial scale vegetation coverage by combining with an unmanned aerial vehicle (UAV) image. The calculation method comprises the following steps: carrying out atmospheric correction and geometrical correction on a remote sensing image, calculating NDVI (Normalized Difference Vegetation Index) and obtaining an effective region according to a predetermined threshold value; splicing UAV pictures and obtaining an orthoimage, registering satellite data subjected to geometrical correction with the spatial position, selecting a typical sample area from the UAV image, and interpreting proportions of all ground objects in the typical sample area by using unsupervised classification; randomly selecting one part of the sample area, and solving the reflectivity of all ground object end elements by using the proportions of all the ground objects in the sample area and the corresponding satellite remote sensing band reflectivity and combining with a least square method; solving the vegetation coverage of all pixels in the effective image area by using a spectral decomposition model and the reflectivity of all the ground object end element; correcting calculation results of the vegetation coverage by using data of a residual sample area. The core of the calculation method disclosed by the invention is based on a method of acquiring the end-element reflectivity of the UAV and a vegetation coverage correction model, and the calculation accuracy of the large spatial scale vegetation coverage can be effectively improved.

The invention discloses a chlorophyll calculating method based on remote-sensing images and a water ecological model. The method comprises the steps of 1, based on remote-sensing image data, data preprocessing like radiometric calibration, atmospheric correction and image cropping and fusing is conducted; 2, a remote-sensing water quality inversion model is constructed, and respective inversion ofindexes like water temperature and chlorophyll concentration is achieved; 3, various index concentrations and future weather change which are obtained through inversion of the remote-sensing images are regarded as input conditions of the water ecological model, and the chlorophyll concentration dynamic change process of a water area environment is simulated within a future duration caused by change like nutritive substance conversion and algae growth and death under a future weather change condition. The chlorophyll calculating method is applicable to water environment monitoring and predicting, and has popularity and high calculation precision, chlorophyll concentration prediction and simulation of a large area of a drainage basin are achieved in real time under the condition of the limited monitoring site, and an integrated chlorophyll concentration predicting system integrating the remote-sensing images and the water ecological model can be formed.

The invention discloses a permeable band ratio factor-based water depth inversion method, which comprises the following steps of: 1, performing radiant calibration on a remote sensing image; 2, performing atmospheric correction on the remote sensing image subjected to the radiant calibration; 3, performing image enhancement on the remote sensing image subjected to the atmospheric correction; 4, performing amphibious separation on the remote sensing image subjected to the image enhancement; 5, performing geometric correction and data processing on the remote sensing image subjected to the amphibious separation; and 6, establishing a neural network water depth inversion model. Compared with a neural network model using single permeable band as input, the permeable band ratio factor-based neural network inversion model has low standard deviation and large related coefficients. The model is not affected by the seawater type and the undersea sediment, and simultaneously has the function ofsolving nonlinear mapping and good promotion capability.

The invention discloses a remote sensing image atmospheric correction method which comprises the following specific steps of: 1) constructing a look-up table; 2) calculating the apparent reflectance; 3) acquiring angle information; 4) acquiring the optical thickness of aerosol: obtaining the optical thickness of aerosol by utilizing HJ--1 satellite CCD (Charged Coupled Device) data inversion based on a pure pixel method; 5) obtaining atmospheric correction parameters by utilizing pixel imaging geometry and optical thickness interpolation; and 6) carrying out atmospheric correction on pixels one by one, so as to obtain the reflectance ratio of the ground surface. Compared with a dark pixel method, the remote sensing image atmospheric correction method has the advantages that the obtained reflectance ratio is more approximate to the actually measured reflectance ratio of the ground surface, and the correction accuracy is greatly improved.

The invention discloses a method for joint inversion of forest aboveground biomass by integrating high-resolution CCD data, hyperspectral image data and laser radar point cloud data. According to themethod, first, geometric correction and stitching preprocessing are performed on airborne high-resolution CCD images, geometric correction and atmospheric correction preprocessing are performed on hyperspectral images, filtering and interpolation are performed on the laser radar point cloud data to generate a digital terrain model, and the point cloud data is normalized; second, texture features,spectral features and point cloud structural features are extracted based on three data sources obtained after preprocessing respectively; and last, models are constructed respectively in combinationwith ground measured data and extracted feature variables to predict the forest aboveground biomass. Through the method, the forest aboveground biomass of a subtropical natural secondary forest is extracted; and compared with aboveground biomass estimation results obtained by using other approximate remote sensing methods, the relative root-mean-square error of the method is lowered by 10% or above.

The embodiment of the invention provides a subway subgrade structure monitoring method and device based on foundation InSAR. The method comprises the steps of using a radar sensor for repeatedly observing a target area on a linear scanning slide rail, and obtaining a synthetic aperture radar SAR image in about 10 seconds; matching image elements representing the same object in the target region inthe two SAR images to the same position, carrying out image registration; subjecting an interference image pair after image registration to conjugate multiplication (as shown in the description) to obtain an interference phase graph; carrying out noise filtering processing on the obtained coherent graph gamma; carrying out phase unwrapping on the coherent graph gamma subjected to noise filteringprocessing; carrying out atmospheric correction on the coherent image gamma subjected to phase unwrapping to obtain a deformation graph of the target area; carrying out geocoding on the deformation graph of the target area under the radar coordinate system, projecting to the geographic coordinate system, and obtaining a geographic coded deformation graph. High-precision and continuous deformationmonitoring of the whole area of the subway elevated and roadbed structure can be implemented under the extreme weather conditions or long distances (4Km) range.

The invention provides a Beidou short message technology based multi-mode and multi-frequency maritime precisely positioning method which comprises the following steps: first, using an automatically downloaded and updated IGU ultra-fast track to solve the track correction information, the clock correction information and the atmospheric correction information; second, conducting real-time RTCM data stream decoding; and accessed by data stream, using the IF model and the MW model respectively to calculate the correction information of the observation value and the wide-lane ambiguity information; carrying out reasonable transformation to the solved correction information; extracting the effective fractional part of the information for decoding and using the Beidou short message technology to broadcast it to a user. And while a mobile station receives real time data streams, it receives the Beidou short message data and decodes the data for the recovery of effective correction information. According to the invention, the IF model and MW model are used to seek the real-time dynamic position of a mobile station through the PPP technology; and the method attempts to achieve fixed ambiguity so as to provide the user with high-precision positioning. Compared with the prior art, the method of invention is highly precise and stable. The method can also be applied widely and practically.

The invention discloses a high-resolution remote sensing data atmospheric correction method comprising the following steps of: S1, obtaining related information from data element information; S2, calculating the apparent reflectance of the typical land surface; S3, obtaining a mask of the typical land surface; S4, building an AOD (Aerosol Optical Depth) inversion correction lookup table by utilizing an atmospheric radiation transmission model; S5, calculating the AOD of the typical land surface; S6, obtaining a land surface classification chart; S7, forming the AOD of an whole image through a spatial expansion method; and S8, executing atmospheric correction on the high-resolution remote sensing data according to the AOD of the whole image and the atmospheric correction lookup table to get the land surface reflectance of the high-resolution remote sensing data. In the method, the high-resolution satellite remote sensing data is used for obtaining the AOD of the whole image, the accuracy and usability of the atmospheric correction are improved, and automation of the whole flow from AOD to atmospheric correction is realized.

The invention discloses a river and lake water quality monitoring method based on high-resolution satellite images. The river and lake water quality monitoring method comprises satellite image radiometric calibration, atmospheric correction, RPC orthographic correction, image splicing, automatic water body extraction, water quality quantitative inversion modeling, water quality quantitative inversion model precision verification and water quality quantitative inversion model application. The invention relates to the technical field of inland water environment remote sensing science, in particular to a river and lake water quality monitoring method based on a high-resolution satellite image, which has the advantages of wide monitoring range, high speed, low cost and convenience in long-termdynamic monitoring by utilizing a remote sensing technology. A multiple linear regression model between different water quality parameter concentrations and image waveband reflectivity is establishedby combining sampling data of a water quality monitoring station and representation of various substances in a water body on a remote sensing image, and a relative error and an absolute error of themodel are calculated according to an inversion result, so as to promote the model, and water quality evaluation of the whole river and even a water area with a larger range is achieved.

The invention discloses an analysis method of remote sensing inversion of water color parameters of inland class II water. According to the method, inherent optical quantity of the class II water is inverted with an improved QAA (quasi-analytical algorithm), and concentration inversion of the water color parameters such as chlorophyll a and suspended matters is realized based on the inverted inherent optical quantity of the class II water. The method comprises specific steps as follows: (1), data of remote sensing reflectance above the surface of the water is input, parameter inversion of the inherent optical quantity of the water is realized based on the improved QAA, and absorption coefficients and scattering coefficients of the water and absorption coefficients of phytoplankton are acquired; (2), concentration data of the chlorophyll a and concentration data of the suspended matters of the water at a sampling point are input, a chlorophyll a concentration quantitative inversion model is established according to the concentration data of chlorophyll a and the absorption coefficients of the phytoplankton, and a suspended matter concentration quantitative inversion model is established according to the concentration data of the suspended matters and the absorption coefficients of the water after removal of pure water; (3), hyperspectral data finishing atmospheric correction are input, and concentration inversion of the water color parameters of the inland class II water in a monitoring area is realized according to the step (1) and the step (2).

The invention provides a method and a device for identifying black and smelly water. The method comprise the following steps: preprocessing an initial high-spatial-resolution remote sensing image to obtain a preprocessed high-spatial-resolution remote sensing image; extracting each pixel of the water from the preprocessed high-spatial-resolution remote sensing image, and obtaining Rayleigh scattering reflectivity of blue light band, Rayleigh scattering reflectivity of green light band and Rayleigh scattering reflectivity of red light band of each pixel of the water; calculating BOI index valueof each pixel according to the Rayleigh scattering reflectivity of blue light band, the Rayleigh scattering reflectivity of green light band, the Rayleigh scattering reflectivity of red light band and a preset BOI index model; comparing the BOI index values with a preset threshold value to determine the black odor level of each pixel in the water. Thus, the black and smelly water is identified with a remote sensing technology on the basis of the Rayleigh scattering reflectivity of different bands and the BOI indexes without accurate atmospheric correction.

The invention discloses a lake water body blue-green algae abundance estimation method based on remote sensing. The method comprises the following steps: carrying out high-spectrum remote sensing measurement on a lake water body by utilizing the concentration ratio of phycocyanin and chlorophyll to represent the blue-green algae abundance, selecting the ratio of remote sensing reflective rates of the two specific wave lengths and a function thereof as characteristics of blue-green algae, establishing an estimation model of the lake water body blue-green algae abundance, and estimating the lake water body blue-green algae abundance based on remote sensing. The method provided by the invention adopts a surface feature spectrograph or an onboard high-spectrum imager to carry out high-spectrum remote sensing on the lake water body, and the remote sensing reflective rate required for estimation can be obtained through performing radiation correction and atmospheric correction on aviation satellite high-spectrum data. The method provided by the invention has the advantages that the accuracy is high, the model is simple, the remote sensing reflective rates of the two wave lengths are selected, not only can the estimation on the water body blue-green algae abundance measured by the surface feature spectrograph be realized, but also the estimation on the blue-green algae abundance by virtue of aviation high-spectrum remote-sensing images is realized.

The invention discloses an annual regional vegetation coverage calculation method. The method comprises steps that accurate radiation calibration, atmospheric correction and geometric registration arecarried out for all remote sensing images of the same annual region, and a remote sensing image result set is acquired, based on the constructed cloud layer index, the shadow index, the background index and the vegetation index, the corresponding special information is extracted; remote sensing images of different time phases are processed through utilizing the de-cloud algorithm and the de-shadow algorithm respectively; spatial synthesis of the single phase vegetation index information is carried out; fusion of the annual multi-phase information is carried out, the spatial distribution map of the annual regional vegetation index information is calculated; and lastly, the spatial distribution map of the annual regional vegetation coverage information is calculated. The method is advantaged in that influence of weather, terrain, phase, vegetation type and environment factors on the vegetation coverage rate is eliminated, an annual regional vegetation coverage calculation problem is solved, accuracy and reliability of regional vegetation coverage calculation are improved, the operation process is simple and flexible, and the method is easy for popularization and application in the regional scale.

The invention provides a method and a system for inverting urban surface temperatures. The method comprises the steps of S1, obtaining the TM image data of a region to be tested, S2, preprocessing the TM image data, including atmospheric correction, geometric correction, image enhancement and image clipping, S3, converting the pixel gray level value of the thermal infrared band in the preprocessed TM image data into corresponding radiance, S4, obtaining corresponding a radiation brightness temperature according to the radiance, and S5, converting the radiation brightness temperature into the surface temperature by use of the emissivity of different surface features, wherein the TM image data are multi-band scanning image data obtained by use of an optical earth land satellite Landsat-5 thematic charting instrument TM. The method can be widely applied to monitoring the urban thermal environment, and has the advantages of low cost, high accuracy and wide application range.

The invention discloses an atmospheric correction method for multispectral data of a remote sensing satellite. The method comprises the following steps of: (1) reading data, and converting the data into apparent radiance data and apparent reflectivity data; (2) calculating decision factors, i.e. a ratio vegetation index, a soil adjustable vegetation index and a normalization water body index in adecision tree method according to the apparent reflectivity data; (3) acquiring apparent reflectivity data in a dark target area according to the apparent reflectivity data, and calculating air path reflectivity; (4) determining the aerosol optical depth of each multispectral waveband according to the relation among the air path reflectivity, a Rayleigh reflectivity ratio and the aerosol optical depth, and determining the total optical depth of a position of 550 nanometers according to the aerosol optical depth and the Rayleigh scatting optical depth of each multispectral waveband; and (5) acquiring an inversion parameter from a lookup table according to the total optical depth of the position of 550 nanometers, and solving land surface reflectivity through a radiative transfer equation to finish atmospheric correction.

A system and method crowdsources atmospheric data from one or more rovers. The rovers calculate an estimated ionosphere delay value that indicates an adverse effect of ionospheric activity on signals received from the GNSS satellite. The values and identifiers may be transmitted to a server. The server utilizes the received information to generate an ionosphere map that reflects the magnitude of ionospheric delay at different locations. The ionosphere map is transmitted to one or more rovers. The rover determines if a pierce point associated with a selected GNSS satellite in view of the rover falls within the boundaries of the ionosphere map. If so, a corresponding ionosphere delay value is obtained utilizing the ionosphere map and then applied as a correction to account for ionospheric activity. In addition, the central server and/or rover may transmit the estimated ionosphere delay values and identifiers to other rovers.

The invention relates to a hyperspectral identification method for a land parcel-based crop variety, which comprises the following steps: firstly, performing pretreatment on Hyperion data so as to remove unscaled bands which are easily influenced by water vapor in the Hyperion data; performing atmospheric correction on the data by utilizing a Flaash atmospheric correction module of ENVI; then, performing geometry correction on the Hyperion data by utilizing a topographic map or satellite data, such as corrected SPOT5, TM and the like to obtain a corrected Hyperion reflectivity image; performing outfield global positioning system (GPS) measurement on a crop variety land parcel to obtain the land parcel distribution map of the crop variety; overlying land parcel base onto the Hyperion reflectivity image to compute the characteristics of the crop variety, such as reflectivity mean value, variance and the like; by taking the reflectivity mean value, the variance and the like as the characteristics, performing image segmentation on the Hyperion reflectivity image to obtain the land parcel data based on the Hyperion reflectivity image; and according to the characteristics of the crop variety, such as the reflectivity mean value, the variance and the like, performing variety classification on the land parcel data to obtain a land parcel-based crop variety distribution map. In the hyperspectral identification method for the land parcel-based crop variety, the Hyperion hyperspectral data and outfield crop variety land parcel data are adopted to realize the drafting of the crop variety based on the image segmentation technology. The hyperspectral identification method for the land parcel-based crop variety can be used for monitoring nationwide crop varieties in the department of agriculture, and has wide market prospects and application value.

The invention relates to a hyperspectral remote sensing water depth inversion method based on deep learning, and the method comprises the following steps: carrying out geometric correction on an original hyperspectral remote sensing image of a research region, and carrying out atmospheric correction to obtain the real reflectivity of each waveband; screening an actually measured water depth rangewithin the range of 0-20 meters in the research area; clipping the remote sensing image according to the spatial range corresponding to the screened water depth data, and processing the remote sensingimage into a formatted data file; generating a formatted training data set by matching the spectral reflectivity information of the remote sensing image with the actually measured water depth data according to geographic coordinates; Using Tensorflow and Keras deep learning framework to build a fully connected neural network, 1D- CNN network, 2D-CNN network three deep learning networks to train the research area data; And respectively applying the trained network model to remote sensing image data to invert the water depth of the research area. According to the method, high-precision water depth data can be directly inverted only by taking hyperspectral remote sensing image spectral information of an optical shallow water region as input.