230 results about "Satellite imagery" patented technology

Methods and articles of manufacture for estimating or deriving weather observations/conditions for any given location using observed weather conditions from neighboring locations, radar data, lightning data, satellite imagery, etc. An initial estimate of weather conditions for a location is made based on a downscaling process using the current conditions the neighboring locations. A measure of corroboration between the radar data and surface weather conditions at official observing stations may be established. Through the results of the downscaling process, radar calibration statistics and estimates of ground-based precipitation, the corroboration can be iteratively tuned, resulting in a weather conditions vector containing associated meteorological fields for locations that lie between or near the sparse network of official observing sites from which an estimate of the weather conditions may be made.

A reporting program and system for updating a master copy of a geographic database is disclosed. According to the method and system, reports are obtained that relate to data contained in a geographic database. Satellite images of the geographic locations represented by the data in the reports are obtained. The satellite images are analyzed to confirm a need to make changes to the master copy of the geographic database based on the reports. After confirming the need to make, the changes, the master copy of the geographic database is updated.

The invention provides a method and a system for processing high-definition remote sensing image data by applying a rational function model, a satellite image block adjustment and auto-match and distributed parallel processing technology. The method comprises the following steps: analyzing and processing input primary image data and computing parameters of the corresponding rational function model so as to obtain a stereo image pair by applying the computed rational function model; performing measurement and singlechip orientation of a control point and a connecting point on the primary image data, and performing block adjustment processing on an error between stereo image pixel data and the primary image data so as to improve the fit precision between the stereo image pixel data and the primary image data; generating an image similar to an epipolar ray, defining a matching template, measuring matched seed points and lines, performing automatic image matching and generating a digital elevation model; and respectively generating a digital orthophoto map and a digital line graph, and finishing manufacturing a product by applying the obtained data.

First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.

A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water. A flow simulation is performed to determine regions of the field that are likely to pool water after rainfall based on precipitation data, elevation data, and soil property data of the field. A graph of vertices representing the pixels and edges representing connections between neighboring pixels is generated. The probability of each pixel within the graph being ponding water is set based on the probability pixel being water, the likelihood that water will pool in the area represented by the pixel, the probability of neighboring pixels being ponding water, and a cropland mask that identifies which pixels correspond to cropland. A class for each pixel is then determined that maximizes the joint probability over the graph.

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 steady automatic matching method for high-resolution satellite image connecting points. The method comprises the following steps of: A, performing automatic enhancement by using Wallis filter technology, generating pyramid images of each layer and extracting the images by using feature extraction operators; B, forecasting an initial point position of identical points by using satellite image orientation parameters and upper pyramid matching results of characteristic points, establishing an epipolar geometric constraint equation, and performing geometric coarse correction on matched window images; C, removing error matched points in the matching result of the pyramid images of each layer by using an RFM model-based block adjustment method; and D, repeating the steps B, C and D till the primary image layer, and finally refining the matching result by using a double-sheet least square matching method. The method can greatly reduce the artificial editing workload of connecting point measurement and improve the automation degree of satellite image data processing by combining block adjustment and satellite image matching, and has remarkable economic benefit and social benefit.

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 present invention provides an automatic cloud detection method and system for a multi-spectral remote sensing satellite image. The method comprises: preparing data; roughly extracting clouds; extracting texture information on an image intensity information channel by combining histogram equalization and bilateral filtering; segmenting a texture information map by using a two-dimensional Otsu threshold; removing an error from a rough detection result by using a binary detail map after segmentation; using intensity information of a raw image as a guide map; and based on the rough detection result with the error being removed, performing accurate extraction on the clouds through edge seed expansion. The technical scheme actually realizes automatic, fast, and accurate detection of cloud.

A method enables users to automatically switch available network connections for the current position as indicated by GPS while maintaining Internet connection. The selection of the network connection depends on a network connection profile which may be built up gradually and the score of each connection. Moreover the local network topology for the GPS location may be overlaid on a geographic map as might be generated by satellite imagery to give the user a better understanding of the topology.

Systems and methods for processing satellite imagery include a satellite, a processor, a database of vessel position data and a computer readable storage medium. The methods process satellite imagery by fusing the imagery with information from the database to automatically identify ships. The methods include the steps of defining an Area of Interest (AOI) and Time of Interest (TOI) for the image and enlarging the AOI according to a time window that brackets the TOI and an assumed vessel maximum speed. Vessel position data from the database for all vessels within the enlarged AOI and the time window is accessed, and fused to imagery position data using Chi-Squared probability analysis. If the analysis meets predetermined probability threshold criteria, the vessel position is assigned to the satellite image to identify the vessel. Otherwise, the operator is alerted that imaged vessels do not correlate to vessel reporting data, or vice versa.

The invention discloses an automatic geometric correction and orthorectification method for multispectral remote sensing satellite images of a mountainous area. The method comprises the following steps of: selection and splicing of reference images; automatic selection of object points with same names; screening of the object points with same names, and geometric correction and orthorectification for correcting the images; precision evaluation; and output of the images. The multispectral remote sensing satellite images of the mountainous area, such as images of China domestic environment and disaster monitoring small satellite (with the model HJ-1A/B), have the characteristic of serious geometric distortion, but the automatic geometric correction and orthorectification method can be used for performing the orthorectification on the multispectral remote sensing satellite images of the mountainous area in mass and generating residual error reports of the coherent images by adopting a concept of the object points with same names to implement the geometric correction and orthorectification. The automatic geometric correction and orthorectification method has the advantages of high efficiency and precision, is very efficient when being used for processing the multispectral remote sensing satellite images with mass data of the mountainous area, can remarkably save manpower and material resources needed by the conventional remote sensing image processing process, and particularly is beneficial to the processing of the images of the China domestic environment and disaster monitoring small satellite.

Disclosed is a system and method for collecting, processing and aggregating satellite imagery with large volumes of other digitized data for analysis by a human user in order to identify geographic areas for further data collection and/or analysis. More particularly, the invention relates to identifying geographic subjects for satellite image acquisition by aggregating and analyzing first-time changes detected by satellite imagery as well as weather report data, social media streams and newswire feeds. An analytics engine uses rules to qualify, flag and correlate asynchronous data from a plurality of sources with changes on the earth's surface, and catalogs and stores the qualified and correlated data where it may be queried and used to prepare reports or recommendations for future satellite image acquisition targets.

The invention discloses an associated parallel matching method for multi-source multi-track long-strip satellite remote sensing images. The spatial structure and contrast of local textures of images are subjected to statistical analysis through introducing a characteristic evaluation operator; by using an associated matching strategy for multi-track satellite images, the maximum overlapping times of connecting points among tracks is ensured; under the assistance of global SRTM (shuttle radar topography mission) data, an approximate epipolar geometric constraint is established by using topographic fluctuation changes, thereby greatly reducing epipolar prediction errors caused by elevation errors; a small-surface-element geometric correction method and a control network based matching growth algorithm are comprehensively utilized, and through continuously refining a difference compensation model, geometric and radial distortions among multi-source images are eliminated, and finally, stable and high-precision matching results are obtained; through carrying out integration and improvement on a matching process and a matching strategy, a more perfect matching scheme is provided for the associated processing of multi-source multi-track long-strip satellite images, and a guarantee is provided for the integrated application of massive satellite remote sensing data.

The invention provides an optical remote sensing satellite rigorous imaging geometrical model building method. The optical remote sensing satellite rigorous imaging geometrical model building method comprises the following steps that the geometrical relationship between the image point coordinates of an optical remote sensing satellite and the satellite is determined according to design parameters and on-orbit calibration parameters of an optical remote sensing satellite camera and the installation relation of the camera and the satellite; the shooting position of a satellite image is determined according to the GPS carried by the optical remote sensing satellite, the observation data of a laser corner reflector and the installation relation of the laser corner reflector and the satellite; the shooting angle of the satellite image is determined according to a star sensor carried by the optical remote sensing satellite and the observation data of a gyroscope and the installation relation of the gyroscope and the optical remote sensing satellite, a collinearity equation of all image points of the optical remote sensing satellite is built, and a rigorous imaging geometrical model of optical remote sensing satellite images is formed. The optical remote sensing satellite rigorous imaging geometrical model building method is the basis of optical remote sensing satellite follow-up geometrical imaging processing and application.

The present invention discloses a satellite remote sensing image cloud amount calculation method on the basis of random forest. The satellite remote sensing image cloud amount calculation method on the basis of random forest comprises six steps: sample acquisition, feature extraction, image classifier training, segmentation of image to be measured, image classification, cloud amount calculation and the like. Through adoption of the method provided by the invention, multiple detections may be performed after training just once, an image classifier is obtained through a large number of image trainings, and the image classifier may be used again when cloud detection is performed. The random forest algorithm is low in time complexity at the prediction classification stage, and the cloud zone detection may be rapidly carried out. Through the test, the method provided by the invention is applicable to panchromatic images (ten-dimensional characteristic vector) and also applicable to n-channel multispectral images (10n-dimensional characteristic vector), and has been applied to an actual quality control system of satellite image products, so that the cloud detection of remote sensing images of multiple domestic satellites such as the resource satellite-3, mapping satellite-1, GF-1 and the like are performed, wherein the accuracies reach, respectively, 91%, 88% and 92.4%.

The invention discloses a road survey and design method based on a high-resolution satellite image, which comprises the following steps: A, determining a scope to be covered by a satellite image according to a route scheme, selecting a high-resolution satellite and a sensor for collection, and designing and collecting three-dimensional images of the high-resolution satellite; B, when a scale of 1 to 2,000 is formed, laying and measuring field ground control points and laying control points along the route; C, generating a digital ground model, a digital orthophotomap and a digital line topographic map for the road route area through area adjustment of the three-dimensional images of the high-resolution satellite based on rational polynomial parameters of the images of the high-resolution satellite; and D, calculating engineering quantities and generating route design drawings and tables through measurement of the high-resolution satellite and collaborative design of the road CAD. The invention realizes the measurement of the high-resolution satellite and the collaborative design of the road CAD, thereby greatly reducing the field workload, shortening the survey and design period, and producing significant economic and social benefits.

The invention discloses a content complexity adaptive video satellite compressed image super resolution method comprising the following steps: dividing an observation image into zones of unequal content complexity from the texture thickness and structure simple and complication angles; gathering image samples with the consistent characteristics so as to form training image sets; forming image sets of different attributes, and using each image set to train a depth learning network model; using the model matching with image different zone statistics characteristics to carry out super resolution reconstructions of the corresponding zones. The method considers content complexity differences of satellite image different natural object types, thus effectively improving the video satellite image super resolution reconstruction precision.

One aspect of the present invention can include a method for detecting tsunamis that can include a step of detecting a location of a rapid water displacement event occurring in an ocean, such as an underground Earthquake. Images can be captured proximate to the event utilizing a satellite. The captured images can be analyzed to detect and/or track a tsunami.