1507 results about "Satellite remote sensing" patented technology

The invention discloses a ground daily rainfall predicting method based on satellite remote sensing and regression Kriging. The method comprises the steps that firstly, data are fast obtained through satellite remote sensing, and a regression relation among ground-based observation values, TRMM, DEMs and geographic positions of rainfall capacities of all levels is established according to the classification of the rainfall to obtain regression estimated values and regression residual errors of all levels; secondly, the spatial agglomeration degrees of the regression residual errors of all levels are analyzed, the trend removing is carried out on the regression residual errors, and the Kriging interpolation of the regression residual errors is carried out to obtain the regression residual error spatial distribution characteristics of all levels per 1 km; thirdly, the regression estimated values of all levels and the regression residual errors of all levels are added to obtain the ground-based predicting values of rainfall of all levels per 1 km; lastly, the ground-based predicting values of the rainfall of all levels are merged to obtain a daily rainfall predicting value per 1 km. According to the ground daily rainfall predicting method, the spatial and temporal distribution characteristics of the ground-based rainfall can be accurately predicted, the predicting precision of the ground daily rainfall is improved, the predicted space resolution is improved, and the key problem that the water conservancy department predicts the ground rainfall is solved.

A method and multi-scale attention system for the detection of objects and temporal change regions by a spatiotemporal attention operator of an image sequence, which linearly aggregates temporal change filter with spatial saliency filter and includes an extractor of salient maxima, which selects consecutive salient maxima of the spatiotemporal operator to produce the locations of the objects of interest and the centers of temporal change regions. The concept of spatial local scale introduced into the system and method for its determination allows for a scale-adaptive integration of the temporal change with spatial saliency and effective detection of different in size and location objects of interest. Can be used for object and spatiotemporal change detection in monitoring pollution, natural disasters, weather conditions and environmental changes based on satellite remote sensing imagery from various sensors.

The invention relates to the field of ecological environment monitoring, and discloses a terrestrial plant ecological environment monitoring method based on multi-source remote sensing data fusion, which is used for realizing identification, classification and dynamic monitoring of vegetation types in a target area more efficiently. An unmanned aerial vehicle is used for vertically shooting a target area, and unmanned aerial vehicle remote sensing three-dimensional modeling is generated; Obtaining a spectral remote sensing image of a target area through satellite remote sensing, and fusing thesatellite remote sensing image with a model generated by unmanned aerial vehicle remote sensing three-dimensional modeling; Calculating and segmenting the fused image; Determining characteristic parameter indexes participating in object-oriented classification, and classifying and extracting monitored vegetation types; Constructing a confusion matrix, evaluating the classification precision by using the confusion matrix, and extracting the area size and distribution condition of each type of vegetation; Finally, the classification results of the two stages are subjected to superposition comparison analysis, and the dynamic change ranges and areas of different vegetation types are calculated. The method is suitable for terrestrial plant ecological environment monitoring.

A fast geometric correction method for a satellite remote sensing image and based on a control point image database comprises the following steps: the geographical scope of an image to be corrected is determined; all control points which meet requirements are retrieved in the control point image database according to the determined geographical scope of the image to be corrected; automatic matching and point selection are conducted, and control point pairs used for geometric correction are determined; whether the number and the distribution of the matched control point pairs meet the requirements of the geometric correction is judged, if the judging result is yes, the next step is conducted, and if the judging result is no, the retrieval step is conducted again; a geometric correction model is selected according to the type and imaging features of a sensor; a transformational relation between a pixel coordinate and a geodetic coordinate is established based on the matched control point pairs and the selected geometric correction model; and the geometric correction is conducted based on a digital elevation model and the transformational relation so as to obtain a corrected digital orthoimage. According to the fast geometric correction method for the satellite remote sensing image and based on the control point image database, the automatic image matching technique is introduced into automatic selection of the control points, and automatic geometric correction processing of the satellite remote sensing image can be realized.

The invention relates to a satellite image three-dimensional area network adjustment method based on satellite-borne laser height measurement data. The method comprises the following steps: respectively acquiring first control data and second control data corresponding to a connection point from a first control data source and a second control source; carrying out combined three-dimensional adjustment treatment for the first control data and second control data on the basis of a remote-sensing three-dimensional imaging model, acquiring a parameter correction value of the remote-sensing three-dimensional imaging model, wherein acquiring the first control data and the second control data includes acquiring a plane coordinate of the satellite-borne laser height measurement data; mapping the plane coordinate of the satellite-borne laser height measurement data to a satellite remote-sensing digital orthographic image, and acquiring a footprint image; forming second control data by the satellite-borne laser height measurement data and the footprint image or partial plane control data of the footprint image; acquiring homonymy points of a given quantity in the range of the footprint image or part of the footprint image as connection points, and using the control data of the homonymy points as first control data.

The invention discloses a method for remote sensing monitoring and early warning fire in sylvosteppe, belonging to the field of remote sensing technology application. The invention makes full use of the information expressive force of satellite remote sensing data on a large-scale space, comprises fire risk evaluation on the sylvosteppe ecological system in a large-scale range and detection of abnormal high-temperature points in the sylvosteppe on the basis of the remote sensing data, and generates the fire remote sensing monitoring and early warning information for the sylvosteppe in a point-sphere integrated manner by assimilating and processing the detection data (point) on the abnormal high-temperature points and data of fire risk evaluation (sphere), thus providing decisive instruction information for fire protection and fire emergencies of the sylvosteppe. The invention is characterized by comprising four parts of a data pre-treatment unit, an abnormal high-temperature point detection unit, a fire risk evaluation unit and an early warning information processing unit.

The invention relates to a target detection method based on high resolution optical satellite remote sensing images, and a system thereof. The target detection method based on high resolution opticalsatellite remote sensing images includes the steps: obtaining a marked target positive sample and a marked background negative sample to form a training sample; for the training sample, extracting a plurality of different weak characteristic channels, and according to the plurality of different weak channels, obtaining a candidate region; obtaining the context scene of the candidate region, performing characteristic extraction on the candidate region and the context scene of the candidate region, and fusing the extracted characteristics to form the characteristics of the candidate region; training the training sample, and obtaining a classifier; classifying the characteristics of the candidate region by means of the classifier, and obtaining a target region including the target; and performing duplicate removal on the target region to obtain a detection target. The target detection method based on high resolution optical satellite remote sensing images realizes target detection on remote sensing images with enlarged formats, optimizes the targets with very short interval and the target detection effect with uncommon length breadth ratio.

The invention provides a forestry harmful organism intelligent identification system on the basis of multi-source image information. The system comprises an unmanned aerial vehicle and a ground station; the unmanned aerial vehicle is equipped with a temperature and humidity module, a communication module and a control module and further equipped with a GPS positioning module, a sensing module and an image capture module which are connected with the control module, and the temperature and humidity module is connected with the control module; the communication module achieves communication of the control module of the unmanned aerial vehicle and the ground station; and forestry harmful organism information is extracted through a satellite remote sensing image, an unmanned aerial vehicle remote sensing image and an image identification technology, and an image collected by an image collection module is subjected to image automatic identification through spectral information and time phase information in time sequence image data to obtain the harmful organism occurrence condition. Compared with satellite remote sensing and airborne and spaceborne remote sensing, the forestry harmful organism intelligent identification system on the basis of the multi-source image information is low in cost, high in accuracy and easy to control.

The invention relates to a fire monitoring method, in particular to a visible light-thermal infrared based multispectral multi-scale forest fire monitoring method which utilizes the advantages of a large-scale satellite remote sensing monitoring means and a small-scale near ground monitoring means to invent a novel monitoring method through reasonable configuration and mutual coordination of the two means. In the invention, the spectral features as well as picture pattern and textural features of forest fire points are researched, the forest fire recognition algorithms of visible light images and thermal infrared images are organically combined, and the interference and the like on forest combustion smoke and open fire recognition, which are caused by the cloud, fog, lamplight, red substances and the like of a forest district, are eliminated, so that the visible light-thermal infrared based multispectral multi-scale forest fire monitoring method by which various factor interferences can be eliminated is invented, thus a fire alarm automatic recognition function can be achieved, the manpower cost for 24-hour manual monitoring can be greatly lowered, and the accuracy of forest fire monitoring can be effectively improved.

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 discloses a grassland satellite remote sensing monitoring system and a grassland satellite remote sensing monitoring method. The system comprises a grassland growth potential monitoring module, a grassland grass yield monitoring module and a grass-livestock balance monitoring module, wherein the grassland growth potential monitoring module is used for acquiring a grassland normalized difference vegetation index (NDVI) and a grassland growth index (GI) by processing data of a satellite remote sensing moderate resolution imaging spectrometer (MODIS) in different periods according to the relation between remote sensing information and the condition of grassland vegetation on the ground to reflect the growth potential of the grassland vegetation; the grassland grass yield monitoring module is used for establishing a grass yield estimating model by combining yield measuring data of ground quadrats according to the information of the satellite remote sensing MODIS and inverting the grass yield of grasslands according to the data of the remote sensing MODIS; and the grass-livestock balance monitoring module is used for estimating the grass-livestock balance condition by combining the current grass yield, foraged grass yield and replenished forage grass of natural grasslands according to the grass yield acquired by the grassland grass yield monitoring module.

The invention relates to a satellite remote sensing image large-area seamless orthographic image manufacturing method. The method comprises the following steps: 1) constructing a remote sensing imageimaging geometric model; 2) reading a to-be-matched remote sensing image and DEM data, and matching the DEM data with the to-be-registered remote sensing image data; 3) carrying out remote sensing image ortho-rectification to obtain a corresponding DOM product; 4) according to the position relation between the orthographic images obtained in the step 3), determining an overlapping region between the images, performing matching in the overlapping region to obtain homonymous points, establishing an error equation based on a TPS model for each group of homonymous points, and performing iterativesolution to obtain a correction amount of geometric deviation between the images; And 5) carrying out dodging and inlaying on all the corrected ortho-images to obtain large-area ortho-image map products, and introducing a thin plate spline model in the ortho-image adjustment process to solve the problem of low precision of a traditional geometric correction model caused by a small overlapping range between the images.

The invention relates to the technical fields of remote sensing and spatial data analysis treatment and agronomy, and discloses a regional scale plant disease and insect pest prediction method based on multi-source information. The regional scale plant disease and insect pest prediction method based on the multi-source information comprehensively applies the satellite remote sensing data reflecting vegetation physiological status and the regional scale meteorological data reflecting the meteorological conditions to the prediction of plant diseases and insect pests, thereby overcoming the defect that a traditional disease and insect pest prediction model does not take the influence on the occurrence rate of the plant diseases and insect pests from the vegetation growth status habitat parameter differences among fields into account. The regional scale disease and insect pest prediction method based on the multi-source information takes the vegetation stress conditions and the habitat information of different planting fields into the model input, outputs the occurrence rate of the plant diseases and insect pests in different planting areas through a standard model under a certain field condition, and outputs more accurate information about the predication of the plant diseases and insect pests.

The invention discloses an automatic monitoring and early-warning system for an offshore area environment based on Arduino. The automatic monitoring and early-warning system comprises a data acquisition terminal node, an aggregation node, a monitoring buoy, a remote database server and an environment monitoring center, wherein a main control board of the data acquisition terminal node and the aggregation node is based on an electronic prototype platform Arduino of an open source, a microprocessor with ATmega2560 as a core is adopted, and the collection, storage, treatment and forwarding of marine environment data and location information of monitoring sites are completed through certain customization. The invention further discloses a method for automatically obtaining the marine environment data from foreign satellite remote sensing sites for integration. The automatic monitoring and early-warning system aims to realize on-line real-time dynamic stereoscopic monitoring and can solve the problem that the conventional marine environmental monitoring technology and method are just established on single-point marine environment data transmission and sampling analysis through acquiring the offshore environment data in a real-time manner and integrating satellite remote sensing data.

The invention discloses a large-compression-ratio satellite remote sensing image compression method based on a deep self-encoding network, and mainly aims to solve the problem of low compression ratio in the prior art. The method comprises the following implementation steps: cascading and stacking a plurality of self-encoders to construct the deep self-encoding network; inputting a group of training image data to the deep self-encoding network, and training the network to obtain optimized network parameters in order to obtain a deep compression network and a deep decompression network; transmitting a remote sensing image to be compressed into the deep compression network to obtain high-order sparse features, and quantifying and encoding the features to obtain final compressed code streams; and inversely quantifying and encoding the received code streams to obtain the high-order sparse features, and transmitting the high-order sparse features to the deep decompression network, wherein a final output of the network is a decompressed remote sensing image. Image processing and deep learning technologies are combined, so that large-ratio compression of satellite remote sensing data is realized. Only simple forward transmission operation is required in compressing and decompressing processes, so that high timeliness is achieved, and the storage and transmission burdens of massive remote sensing data are relieved.

The invention discloses a robust multi-source satellite remote sensing image registration method based on mutual information and block random sample consensus. The method includes the following steps: (A) each layer of pyramid images are generated, and feature points are extracted; (B) on the highest layer of pyramid images, the global registration method is utilized to calculate affine transformation coefficients between the reference image and the slave image, and a rotation angle and a resolution difference coefficient between the images are estimated; (C) the initial positions of homonymy points are accurately predicted, geometric rough correction is carried out on matching window images, and normalized mutual information is utilized to search homonymy points; (D) a quadratic polynomial and a block RANSAC algorithm are utilized to eliminate false matching points; (E) step C and step D are repeated until the original image layer, and accurate image registration is implemented on the basis of the linear rubber sheeting method. The method greatly reduces the workload of manual editing in homonymy point measurement, increases the multi-source satellite remote sensing image data processing automation degree, and can bring remarkable economic and social benefits.

The invention provides a method for monitoring the regional evapotranspiration on the basis of remote sensing, comprising the following steps of: (1) obtaining the multi-temporal and multi-resolution satellite remote-sensing image data at a specific region; (2) carrying out remote sensing inversion to the key parameters on the ground surface; (3) calculating the impedance of each pixel during the exchanging process of the geogas energy by a remote-sensing geogas exchanging model, calculating the sensible heat flux used for heating the air above each pixel, calculating the latent heat energy by an energy remainder formula and inquiring into effective surface impedance; (4) establishing a surface impedance model in a daily dimension so as to obtain a daily ground surface impedance; and (5) working out the daily evapotranspiration by applying a P-M model repeatedly and carrying out time accumulation and dimension down-scaling, thus finally determining the regional ground surface evapotranspiration. According to the validation of the practical measurement at multi-regions, the method for monitoring the regional evapotranspiration is proved to be effective.

The invention discloses a dynamic filtering modeling downscaling method of an environment variable on the basis of low-resolution satellite remote sensing data. The dynamic filtering modeling downscaling method comprises the following steps: firstly, carrying out aggregation calculation on 1km environment variable factors including eight pieces of data i.e., a vegetation index, a digital evaluation model, daytime surface temperature, night surface temperature, a topographic wetness index, a gradient, a slope aspect and a slope length gradient, into 25km to serve as independent variables, and taking corresponding 25Km resolution TRMM (Tropical Rainfall Measuring Mission) 3B43 v7 precipitation data as a dependent variable. An M5 method divides data sets formed by each environment variable into different vector spaces according to geographical similarity, then, the most effect environment variable is independently dynamically filtered in different vector spaces, and a divisional multiple regression model is independently established in the corresponding vector space; and the model is finally applied to the 1km environment variable to finally obtain a precipitation product of the 1km resolution. A downscaling result obtained by partitioning and dynamic factor filtering is obviously superior to a downscaling result based on a conventional regression model.

The invention discloses an object-level high-resolution remote sensing image change detection method based on multi-scale fusion. A color texture segmentation algorithm JSEG is introduced to change detection and multi-scale feature extraction and analysis on an object is realized based on a J-image image sequence generated in the segmentation process of the JSEG algorithm. The algorithm makes comprehensive use of the shape, the size and the spectral and texture features of the object, introduces two different multi-scale fusion strategies based on a D-S evidence theory and weighted data fusion and further improves divisibility of changing categories and non-changing categories so as to construct a complete set of object-level change detection frames based on multi-scale fusion. The feasibility and effectiveness of the method are verified by respectively carrying out experiment on a high-resolution aerial remote sensing DOM image and an SPOT5 satellite remote sensing image and comparing detection effects of change detection methods of different object levels and pixel levels.

The invention relates to an ocean spilt oil film thickness hyperspectral remote sensing estimation method based on a parameter lookup table and belongs to the field of ocean environment monitoring research. The method includes the steps of obtaining and pre-processing standard oil film thickness continuous change hyperspectral data by designing a simulation experiment; obtaining and pre-processing ocean spilt oil satellite remote sensing data; resampling a standard oil film spectrum based on a satellite sensor performance index; normalizing spectral reflectivity; building an oil film thickness optical calculating model; building an attenuation parameter lookup table; normalizing the reflectivity of satellite data and conducting mask treatment; inquiring and building an optimal wave band of the satellite data and relevant parameters; and calculating the oil film thickness in the satellite data through the parameter lookup table and the optical model. Compared with a traditional observation means, by means of the ocean spilt oil film thickness hyperspectral remote sensing estimation method, field workload is less, the thickness of the spilt oil film can be quantized, the thickness of an oil film in a spilt oil area can be calculated without entering an ocean spilt oil pollution area, and requirements of ocean split oil emergency monitoring and evaluation can be met.