137 results about "Remote sensing application" patented technology

What is disclosed is a novel system and method for simultaneous spectral decomposition suitable for image object identification and categorization for scenes and objects under analysis. The present system captures different spectral planes simultaneously using a Fabry-Perot multi-filter grid each tuned to a specific wavelength. A method for classifying pixels in the captured image is provided. The present system and method finds its uses in a wide array of applications such as, for example, occupancy detection in a transportation management system and in medical imaging and diagnosis for healthcare management. The teachings hereof further find their uses in other applications where there is a need to capture a two dimensional view of a scene and decompose the scene into its spectral bands such that objects in the image can be appropriately identified.

The invention relates to a method for inversing land surface temperature from MODIS data, which can be applied in meteorology, environmental monitoring, land management, agricultural condition monitoring, disaster monitoring and other remote sensing application departments. The method comprises four steps: the first step is to calculate the transmittance of 31st and 32nd wave bands of MODIS through water vapor content of atmosphere. The second step is to calculate the emissivity of the 31st and the 32nd wave bands of the MODIS sensor through vegetation index NDVI. The third step is to simplify a radiation transmission equation. The fourth step is to carry out inversion calculation on the MODIS data and obtain the land surface target object temperature and the emissivity distribution situation, so that the method can be used in meteorological forecasting, environmental monitoring, agricultural condition monitoring, disaster monitoring and other departments.

The invention discloses a grassland dry matter mass remote sensing estimating method, which belongs to the field of remote sensing application. The method comprises the following steps of: collecting and processing field test data; downloading and processing Modis (Moderate Resolution Imaging Spectroradiometer) satellite data; analyzing and modeling the data and detecting the accuracy and the like. The method for establishing a ground spectrum model for predicating ground dry matter mass and correcting a Modis spectrum model through ground hyperspectral experiments is different from the method for directly predicating biomass liveweight by using NDVI (Normalized Difference Vegetation Index) data of the Modis remote sensing satellite in other like researches so that the grassland dry matter mass remote sensing estimating method is helpful for improving the predicating accuracy, is beneficial for scientific management and reasonable utilization of the grassland resources and has important practical significance in correct evaluation of the real productivity of the grassland and the sustainable use of the grass resources.

The present invention belongs to the technical field of remote sensing application, and discloses a vegetable field monitoring method based on multi-source multi-temporal remote sensing image data. The method comprises the steps of: determining a type and a phenological phase of a vegetable field and an interfering ground object, and selecting optimal phases of vegetable field and interfering ground object identification; acquiring multi-source multi-temporal remote sensing image data and service specific vector data; and by using an object-oriented classification method and combining parcel-level cultivated land vector data, performing vegetable field data extraction, and extracting vegetable data according to the two optimal phases and taking an intersection, so as to obtain a vegetable field monitoring result. According to the method, vegetable field monitoring is performed based on multi-source multi-temporal remote sensing image data, so that influence of interfering ground object is effectively reduced, and monitoring precision is improved.

The invention belongs to the field of airborne laser radar forest remote sensing applications, and in particular relates to a single tree segmentation method based on an airborne laser point cloud aggregation relationship. According to the invention, three-dimensional laser point cloud data of the forest vegetation canopy are acquired by using an airborne laser radar, and through the forest identification, the initial segmentation by a local maximum method, the deletion of a wrongly segmented single tree, the cyclic clustering and the processing by a nearest neighbor method, a single tree image segmentation method segmenting a single tree from the forest based on the original three-dimensional laser point cloud data is established. Compared with a CHM single tree segmentation method, the method of the invention is advantageous in that the information loss can be avoided, segmentation errors and improper shape segmentation are reduced, the segmentation effect is excellent, the computation time is also greatly reduced, echo laser points belonging to a specific single tree can be quickly and accurately segmented from the forest laser point cloud data, and the parameter inversion of a single tree, the reconstruction of three-dimensional structural features of the single tree and the validation of forest vegetation parameter inversion are facilitated.

The invention relates to a method for estimating the temperature of near-surface air near from MODIS data, which can be used for remote sensing application departments, i.e. weather, environmental monitoring, land management, agricultural monitoring, disaster monitoring, and the like. The method comprises three steps: step1: utilizing the near-surface temperature and the emission rate of each pixel of a MODIS data product and the water vapour content value of atmosphere as priori knowledge and input parameters of atmosphere radiation transmission module simulation software MODTRAN4, carrying out forward direction simulation in different areas and seasons aiming at a 29th thermal infrared band, a 31st thermal infrared band and a 32nd thermal infrared band of each pixel of the obtained remote sensing data MODIS and establishing a training and testing database; step2: utilizing a neural network to repeatedly training and testing a training and testing dataset; step3: carrying out back calculation for practical image data of the MODIS to obtain the temperature distribution condition of the air near the earth's surface of an objective area of the earth's surface. The method can be usedfor weather prognosis, the environmental monitoring, the agricultural monitoring, the disaster monitoring, and the like.

The invention relates to a method for inversing soil moisture content from AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data, which can be used for the remote sensing application departments of monitoring weather and environment, managing land, monitoring a drought and a crop growing situation, monitoring disasters, and the like. The method comprises the following four steps of: firstly, simulating the most part of situations on the earth surface and establishing a database by using a theoretical model; secondly, establishing an inverse algorithm by using a simulation database; thirdly, carrying out the inverse calculation of passive microwave data AMSR-E; and fourthly, carrying out proper evaluation, analysis and modification by using observation data of a meteorological department, thereby increasing the precision and the applicability of the algorithm. The method for inversing soil moisture content from the AMSR-E data can be used for the departments of forecasting weather, monitoring environment, agricultural situations and disaster situations, and the like. The microwave radiation frequency arrangement carried by a Fengyun 3 satellite of China is basically similar to the AMSR-E, and the invention can be applied to the Fengyun 3 satellite of China and a microwave radiometer transmitted by China in the future after being modified.

What is disclosed is a system and method for real-time enhancement of an identified time-series signal of interest in a video that has a similar spatial and temporal structure to a given reference signal, as determined by a measure of closeness. A closeness measure is computed for pixels of each image frame of each channel of a multi-channel video to identify a time-series signal of interest. The intensity of pixels associated with that time-series signal is modified based on a product of the closeness measure and the reference signal scaled by an amplification factor. The modified pixel intensity values are provided back into the source video to generate a reconstructed video such that, upon playback of the reconstructed video, viewers thereof can visually examine the amplified time-series signal, see how it is distributed and how it propagates. The methods disclosed find their uses in remote sensing applications such as telemedicine.

The invention relates to a quick parallelization programming template method for a remote sensing image processing algorithm, which comprises the following steps of: 1) extracting a remote sensing image parallelization processing model; 2) constructing a quick parallelization programming template base, wherein the interface abstract design of basic processing steps of a parallelization processing flow in the remote sensing image parallelization processing model is performed by a software design mode, all operation interfaces in programming template types are parallelized by MPI and Pthread multi-threading technology; and the programming template types are packaged into a parallelized programming template base; and 3) inheriting parallelized programming templates. The quick parallelization programming template method for the remote sensing image processing algorithm has the advantage that technicians in the technical field of remote sensing application can inherit the designated remote sensing image parallelization processing model in the template base by a simple class inheritance method and loads a serial algorithm program which is developed by a user independently to realize the quick parallelization method of the remote sensing processing algorithm and achieve relatively high parallelization efficiency, and thus, large-scale remote sensing data processing performance is greatly improved.

Disclosed is a remote sensing image fusion method oriented to water quality quantitative remote sensing application. By maintaining water area extraction accuracy and water area spectral characteristics furthest and combining PCA fusion and SSVR fusion, a two-level fusion method aiming at water quality quantitative remote sensing application is designed, a decision-level object-oriented surface feature classification and interpretation method is introduced in the process of fusion, and processing results have the characteristic that accurate water body object pixel profile and water area spectral characteristics are maintained. A lot of experiment results show that by the remote sensing image fusion method, water area interpretation accuracy reaches 90%, and water area quantitative inversion results are similar to inversion results acquired by directly utilizing hyperspectral or multispectral images.

The invention belongs to the field of hyperspectral remote sensing application, and particularly discloses a method for automatically identifying rock minerals based on spectral information. The method comprises the following steps: scanning rock minerals to be classified by using a hyperspectral scanner to obtain mineral spectral data; storing the obtained data in a database to be detected; extracting a part of data from the database; identifying mineral species information corresponding to the extracted spectral data according to features of the mineral spectral data by using manual interpretation, and storing the extracted spectral data and the corresponding mineral species information as a learning sample library; establishing an artificial intelligence learning system, and performinglearning and training by using the learning sample library; and detecting the spectral data in the database to be detected by using the artificial intelligence learning system after training and learning optimization to identify the mineral species information; and storing an identification result in an identification result database. By adoption of the method, the human resource consumption can be reduced, the working automation degree is improved, the spectral scanning data processing efficiency is improved, and the economic efficiency is improved.

The invention provides a vehicle detection method for a remote sensing application scene, and the method comprises the steps: making a diversified remote sensing image vehicle detection data set through a homography-based data enhancement method; determining candidate area positions and analogy based on a target detection model considering target area characteristics of a remote sensing image vehicle; improving the resolution of the low-resolution remote sensing image based on a super-resolution reconstruction model of the cyclic generative adversarial network; and completing super-resolutionreconstruction and vehicle detection tasks at the same time based on a synchronous super-division and target detection network model of a task driving thought. Compared with a traditional vehicle detection method, the method has the advantages of being high in efficiency, rich in depth feature semantic information and the like; compared with other vehicle detection algorithms based on deep learning, remote sensing image characteristics are considered, and the method has the advantages of being accurate in target positioning, good in generalization performance and the like. In addition, the multi-task learning model improves the vehicle detection precision of the low-resolution remote sensing image.

The invention relates to a method of an optimal arrangement in time of a laser emitter and a detector for a remote sensing application, comprising: —setting a target time unit integration time t_p; —translating said time unit integration time into a reduced time τ_p and its corresponding power increase factor η⁻¹; —activating both the laser emitter, with a power output corrected by η⁻¹, and the detector for a duration of τ_p; —deactivating the emitter and detector after duration τ_p; —keep emitter and detector off for the subsequent duration t_off=t_p−τ_p.The invention further relates to a device implementing said method.

The invention relates to the technical field of massive remote sensing application services, in particular to a massive remote sensing data processing method and system based on a distributed architecture. The method comprises the following steps: S1, acquiring information required to be searched; S2, carrying out massive remote sensing image database retrieval, if a retrieved remote sensing imagefile has no associated map tile service, turning to the step S3, and otherwise, turning to the step S6; S3, constructing a remote sensing image data pyramid online, if remote sensing tile data is selected to be stored and map tile service is issued, executing the step S4, and if remote sensing tile data is selected not to be stored, executing the step S6; S4, synchronously storing the remote sensing tile data; S5, performing remote sensing tile data spatial index storage; and S6, publishing and displaying the map tile service. Through the distributed architecture, the requirements of massiveremote sensing data storage, management, visualization and data service one-stop processing in the big data era are met.

The invention belongs to the field of hyperspectral remote sensing application and in particular relates to a spectral matching and recognition method. The invention overcomes the defects that the conventional spectral matching and recognition method only considers the whole similarity measurement between spectral lines but neglects the local difference measurement between the spectral lines, and provides a spectral segmentation-based matching and recognition method. The method comprises the following steps of: firstly, performing transformation processing on the spectral lines by adopting the multi-scale wavelet transform taking a second-order Gaussian derived function as a wavelet function; secondly, extracting an optimized inflexion of a spectral curve by using the designed inflexion multi-scale optimizing algorithm; and finally, segmenting the spectral lines based on the extracted optimized inflexion information, and recognizing the spectral lines by adopting a segmentation matching method. The spectral matching and recognition method has the advantages that: wave bands with larger ground object spectrum difference and wave bands with smaller spectrum difference can be segmented into different segmentations through the inflexion segmentation so as to protrude the wave bands with the larger spectrum difference and enhance the effectiveness of spectral matching and recognition.

The invention discloses a shallow sea water depth multi-spectral satellite remote sensing inversion method in a waterless depth control point area, and belongs to the technical field of the satelliteocean remote sensing application. Based on a derived dual-band linear shallow sea water depth inversion model, solving an optimal band rotation unit vector through different depths of different submarine types of pixel points dataset, estimating subsea parameters in the model based on a typical substrate type pixel adoption of a position of a land and water boundary line, calculating a blue-greenband diffusion attenuation coefficient ratio and based on deep water area data closest to a shallow sea area through the same type of substrate and different depths of pixel data, and calculating thegreen band attenuation coefficient based on a semi-analytic and diffuse attenuation coefficient algorithm. According to the shallow sea water depth multi-spectral satellite remote sensing inversion method in the waterless depth control point area, the shallow sea water depth remote sensing inversion in the waterless depth control point area is realized through the calculation of the above parameters.

The invention belongs to the technical field of remote sensing applications and particularly relates to an improved diurnal temperature cycle model (IDTCM) based remote sensing ground surface temperature and time normalization method. The IDTCM based remote sensing ground surface temperature and time normalization method comprises the following steps that data pretreatment is performed, improvement is performed based on DTCM to obtain an IDTCM, remote sensing ground surface temperature and time normalization is performed and TASI aerial remote sensing ground surface temperature and time normalization is performed. By means of the method, the improved diurnal temperature cycle model is established, and the shortcoming that an existing model cannot overcome wind speed fluctuation is overcome. The remote sensing ground surface temperature and time normalization method is established based on the improved diurnal temperature cycle model.

Disclosed is a hyperspectral-imager index optimization method based on remote-sensing application. Firstly, a hyperspectral data application objective is determined and according to the application objective and load design index requirements, hyperspectral load performance index parameters and design index parameters are preset; then an atmospheric radiation transmission model is used to perform hyperspectral signal simulation before pupil entrance of a remote sensing load; then according to a push-broom hyperspectral imaging principle, a hyperspectral imaging simulation model is established so that imaging simulation of the hyperspectral load at a space dimension and a spectral dimension is realized and hyperspectral imaging data and hyperspectral load performance index parameter values are obtained and a hyperspectral data processing method is used to obtain an application result and the relation between an application effect index and a load performance index is established; and at last, an optimization index is determined through a least square method. The hyperspectral-imager index optimization method based on the remote-sensing application introduces a remote-sensing application index into a load index optimization design and considers hyperspectral load index optimization from an application angle so that a reliable and effective means is provided to hyperspectral load design and hyperspectral data application performance evaluation.

The invention relates to a hyperspectral reflectance data spectrum characteristics extracting method based on global sensitivity analysis. The method comprises the following steps of (1) sensitivity analysis: calculating sensitivity of the hyperspectral reflectance data on each waveband position by utilizing a global sensitivity analysis method; (2) characteristic waveband selection: selecting a characteristic waveband according to the sensitivity analysis result; (3) canonical transformation: calculating a characteristic vector by utilizing a canonical transformation method when the divisibility of the class is maximal; (4) spectrum characteristic establishment: establishing a first canonical axis to be used as a spectrum characteristic by utilizing the linear combination of the characteristic waveband reflectance value and the elemental value corresponding to the characteristic vector. Compared with the traditional hyperspectral reflectance data spectrum characteristics extracting method, the spectrum characteristic established by adopting the method enables the divisibility of different classes to be maximized while the data dimension is reduced, and the method is particularly applicable to the remote sensing application problem such as crop stress detection, target recognition, ground feature classification and the like, and has wide prospect in the technical field of the hyperspectral reflectance data processing and application.

The invention discloses a fragmented remote sensing image synthesis method and device for a cloudy and rainy region, and the method comprises the following steps: obtaining a multi-satellite and multi-phase initial image data set of the cloudy and rainy region; carrying out the radiation difference correction of a sensor and an external factor for the initial image data set, so as to obtain a first image data set consistent with the radiation features; obtaining a cloud layer and shadow distribution range of the first image data set, and obtaining a cloudless data fragment set of the cloudy and rainy region through mask processing; selecting a reference data subset from the cloudless data fragment set, repairing a loss region through employing other cloudless data fragment sets, and obtaining the complete cloudless synthetic image of the cloudy and rainy region. The method makes the most of conventional useless data (high cloud images) for synthesis, effectively improves the space-time coverage frequency of the remote sensing observation of the cloudy and rainy region, and provides powerful data support for the remote sensing application based the domestic satellite data especially the application with the high requirements for timeliness.

The present invention provides a process automation method and system in a remote sensing application. The method comprises: creating a visual remote sensing application process model by means of a process model editor; converting the visual remote sensing application process model into a process description language; and parsing the process description language by means of a process scheduling engine and executing a corresponding remote sensing application process. According to the method and system provided by the present invention, a complete process from process model editing, process description language generating to a process scheduling engine is realized; the problems of relatively complicated usage of traditional process scheduling software and a variety of limitations to an operation of a complex process are solved, so that a complex and varied processing flow in the remote sensing application is satisfied.