39 results about "Satellite image processing" patented technology

Systems and methods for generating a crop yield estimate for an area as small as an individual field from images captured by a satellite are disclosed. The system generates simulations of crop yields in a region that includes the area by applying combinations of different parameters to a crop yield models. Observable quantities for simulated yields are determined from the simulations. The simulations and the observable properties are used to train a statistic model for the region that has two or more variables. Images captured by a satellite that include at least a portion of the area are obtained. Crop information is then determined from the images and weather information associated with the dates that the images where captured is obtained. The statistical model is then applied to the crop information and the weather information to determine a crop yield estimate.

The invention discloses a multi-task master control system for a remote sensing satellite image processing load. A CPU+2FPGA framework is adopted, wherein an embedded real-time operating system is transplanted into a CPU to serve as a master control management module for completing state monitoring and task scheduling of the remote sensing satellite image processing load, which has high flexibility, safety and stability; an FPGA can overcome the defects of the CPU with different interrupt priority levels and limited interrupt vector tables under multiple time sequence interfaces when used for integrating and issuing communication information, and can meet the requirements for real-time performance and low delay under a large data size when used for preprocessing images. The system structure design can effectively achieve the master control management function, meet the requirements for highly parallel processing of images and operation of complex floating-point numbers, and meet the requirements for safety, reliability and high flexibility of the system.

The invention discloses an image edge extraction method and system based on the optical spin hall effect space optical field differentiator. The space optical field differentiator is simple in structure and wide in material selection range, large in wavelength response range, easy to prepare and integrate and the like, and the image edges are extracted and processed. According to the invention, the coherent light is used as a carrier to load the image information as an input light field, so that the input light field of p/s polarization state is obliquely incident on the interface of the two different media or the interface of the medium and the metal. Due to the optical spin hall effect, the reflection or transmission field of the corresponding s/p polarization state is used as a one-dimensional first-order space differentiation result of the input light field, so that the edge extraction processing of the image information is realized. The edge extraction method provided by the invention is a super-fast and real-time, the large-flux image processing method, and an important technical application prospect in medical and satellite image processing is provided.

The invention discloses a multi-source satellite image real-time online processing technical method and device. The computer network technology, the computer parallel technology, the computer multithreading technology and the image processing technology are comprehensively used for multi-source satellite image real-time online processing. The satellite image processing is fast, the computing resource use efficiency is high, and the data packet throughput rate is large. The multi-source satellite image real-time online processing technical method includes that a data packet is received on a high speed network, and the received data packet is organized to be an image; the organized image data are analyzed, and the analyzed image is subjected to storage format conversion and processing; the processed image data are continued to be compressed; and the compressed image data packet is sent back to the high speed network.

The invention discloses an image edge extraction method and system based on a surface plasmon spatial light field differentiator. The conventional Fourier optical information processing is based on a lens system, wherein the lens system is large in structural size, and is not liable to be integrated. According to the invention, the surface plasmon spatial light field differentiator is designed, and the surface plasmon spatial light field differentiator is simple in design, wherein the thickness of the surface plasmon spatial light field differentiator is at a nanometer scale. The surface plasmon spatial light field differentiator is easy for large-scale preparation, can be integrated with a silicon-based device, and thereby achieves the processing of the image edge. The method comprises the steps: exciting the surface plasmon on a metal and medium interface based on the characteristics of a metal surface plasmon, controlling the spatial propagation leakage rate and the attenuation rate caused by the material loss to meet a critical coupling condition, and achieving the first-order differential processing of an inputted spatial light field, thereby completing the edge extraction of an input image. The edge extraction method provided by the invention is a superfast, real-time and large-flux image processing method, and is good in application prospect for medical and satellite image processing.

The invention discloses an image stabilization-based remote sensing video satellite image processing method and system, and the method comprises the steps: carrying out the cataloguing processing of each frame, extracting the imaging time of each frame, enabling the imaging time of each frame to serve as a part of an image name, and enabling a plurality of image frames to form an image frame sequence group according to the time sequence; performing relative radiation correction on each frame of the sequence group to eliminate atmospheric radiation; after radiation correction, performing systemgeometric correction on each frame of the sequence group to eliminate geometric distortion generated in the frame during imaging; Taking the previous frame as a reference frame, and carrying out image stabilization processing on each current frame except the first frame in the sequence group, including motion estimation, motion compensation and image correction; and combining the image frame sequences subjected to image stabilization processing to form a video. In the processing process of the remote sensing video satellite, the imaging characteristics of a general remote sensing satellite and a video satellite are considered, and the processing process obviously improves the quality of the remote sensing video.

The invention provides a satellite image processing method and device, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining a satellite image; processing the satellite image by utilizing a semantic branch, a pixel feature branch, a height branch and an offset branch of the first building detection model to obtain a first image feature indicating a building area in the satellite image, a second image feature consisting of pixel features of each pixel of the satellite image; a third image feature comprising the height feature of each pixel in the satellite image, and a fourth image feature comprising the offset feature of each pixel; dividing the building area into sub-areas corresponding to the building according to the second image features; anddetermining the height and the offset of the building according to the height characteristics and the offset characteristics of the pixels in the building subareas, thereby determining the bottom area of the building. According to the scheme, the plurality of branches can perform parallel processing on the satellite image, so that the scheme has higher processing efficiency compared with the prior art.

A satellite image low-coherence region identification method and device based on deep learning belong to the field of satellite image processing, and are characterized in that a quality map obtained by SAR image processing is combined with semantic segmentation to form a process learning framework, and a deep convolutional network is introduced for identification. Compared with manual interpretation and a traditional machine learning algorithm, the low-coherence region recognition method based on deep learning greatly reduces dependence on artificial feature engineering and sample data, and a large-range region can be quickly processed based on high efficiency of convolution calculation; meanwhile, on the basis of introducing global attention, relation representation between pixel positions and semantic categories is further described, the recognition effect of inherent high-noise areas of the SAR image is relieved, the recognition precision is improved on the premise that the calculated amount is not remarkably increased, and low-coherence areas are accurately extracted.

The invention belongs to the technical field of satellite remote sensing information intelligent processing, and provides a multi-view optical satellite image target recognition deep learning training sample set construction method. The method includes: screening, collecting and sorting three-dimensional model data and digital line drawing information of the target area, evaluating and analyzing the consistency of the scale, the resolution and the timeliness of the three-dimensional model data and the digital line drawing information, screening data with relatively high consistency, and if the data does not meet the requirements, re-collecting the data until the requirements are met; and based on target labeling and satellite image imaging full-link simulation of a three-dimensional scene, labeling and augmenting the obtained multi-view satellite image. According to the method, the problem that the existing remote sensing image target detection data set cannot meet the multi-view optical satellite image processing requirement is solved, and a technical foundation is laid for the application of the multi-view satellite image in target recognition.

The invention belongs to the technical field of remote sensing data application and satellite image processing and particularly relates to a method for automatically classifying a satellite image scene based on morphological component analysis. The method comprises the following steps of (1) constructing a dictionary matrix through an independent meta-analysis method; (2) decomposing morphological components of a satellite image based on the morphological component analysis theory; (3) decomposing the satellite image into a texture layer and a bottom layer; (4) performing quantitative calculation on the total probability of classification of an objective image scene according to a mechanism of maximum likelihood estimation with combination of features of the texture layer and the bottom layer, and automatically classifying the satellite image scene with the quantitative calculation on the total probability of classification of objective image scenes as the basis. The method combines with a machine vision method and can be applied to a computer easily. System construction is performed more quickly and accurately through the computer than through a manual method, artificial subjective factors are eliminated, and cost is reduced. Furthermore, the method has high automatic identification accuracy and identification stability.

The invention belongs to the technical field of satellite image processing, and in particular relates to a method for rapid registration of domestic remote sensing satellites based on parallel computing, including methods such as multiple images. The impact of uneven illumination is great, the registration speed is too slow, and it does not meet the requirements of processing the data on the same day and providing public services on the same day. There are few processing methods for domestic satellite remote sensing data. For example, there are rotations, Problems such as panning and zooming can quickly complete the processing of satellite images of various scenes in a streamlined and fully automated manner, unify the coordinates of multi-source satellite images and publish maps online for browsing, and the deformation of ground objects caused by different time phases and different inclination angles 1. The multi-source remote sensing satellite image registration has high precision, fully utilizes hardware resources, and improves the processing efficiency of large-scale parallel registration of massive remote sensing images.

The invention discloses a multi-wavelength space light field differential operation device based on a diffraction grating, and belongs to the field of optical information processing. The spatial lightfield differentiator designed by the invention has the advantages of capability of simultaneously working at multiple wavelengths, simple design, micro-nano device thickness, easiness in large-scalepreparation, capability of being integrated with a silicon-based device and the like. According to the invention, the waveguide mode in the dielectric waveguide layer is excited, and the transfer function of the device is linear, so that first-order differential processing is carried out on an input space light field. According to the invention, a plurality of different waveguide modes can be correspondingly excited at a plurality of wavelengths at the same time, and first-order spatial differential operation of a light field can be realized at the plurality of wavelengths at the same time. The multi-wavelength space light field differential operation device can be used for ultra-fast, real-time and large-flux image processing such as edge extraction, and has important technical application prospects in medical and satellite image processing.

The invention belongs to the technical field of satellite image processing, and in particular relates to an automatic remote sensing image processing method based on parallel computing and distributed storage, including multi-task processing and single-task processing. The problem of limited capacity, data security, and troublesome data retrieval can not only support the storage of large files, but also efficiently store small files, so that users can browse images quickly and seamlessly, and improve the efficiency of automatic image processing while ensuring the processing effect beneficial technical effects.

The invention discloses an SAR image automatic colorization method based on GANIlla, and relates to satellite image processing. The method comprises steps of data set pairing: randomly generating sites on a map, taking a selected site expansion matrix as a selection area, respectively extracting a VV wave band in Sentinel-1 and B4, B3 and B2 wave bands of Sentinel-2 as RGB values, and visualizing data in a JSON format based on python to obtain a color migration data set; dividing the obtained data into two types, namely data of a land area and data of an ocean area; increasing the data of the land area through rotation, local amplification and overturning; carrying out wiener filtering and random color adding respectively on the data of the ocean area; and carrying out training on the Sentinel data after the data is increased by using a GANIlla network. The complex work of establishing a network is avoided, the data pairing speed is improved, the data acquisition difficulty is reduced, and the remote sensing image quality is greatly improved.

The invention relates to the technical field of satellite image processing, in particular to a satellite image preprocessing system, which comprises a shooting assembly, a driving assembly and a controller. The shooting assembly comprises a mounting seat, a first camera, a second camera and a vertical camera; a vertical camera is vertically mounted in the center of the bottom of the mounting base,and a first camera and a second camera are arranged on the two sides of the vertical camera correspondingly; driving assemblies are arranged on the sides, close to the mounting base, of the first camera and the second camera; each driving assembly comprises a first guide rail, a second guide rail, a driving block and a connecting rod; the driving blocks are slidably connected to the first guide rails, the first guide rails are vertically arranged on the second guide rails and slidably connected to the second guide rails, one ends of the connecting rods are hinged to the driving blocks, and afirst camera or a second camera is fixed at the other end of the connecting rod. By arranging the controller and the driving assembly, the number of times of photographing and shooting is reduced, andthe efficiency is improved.

The invention provides a satellite image processing system and method based on high-performance calculation. The system comprises a plurality of image processing computers, a plurality of image data storage computers and a management computer which are connected through an internal network, and one or more client computers which are connected with the management computer through an external network. According to the invention, a plurality of image processing computers are connected together through a high-speed network so as to provide strong computing power; an independent and extensible storage system is built through a plurality of image data storage computers, and large-capacity and high-performance shared storage is provided; the processing performance and the storage performance are allowed to be expanded by increasing more computers; multiple users are supported to carry out image processing operation at the same time, and resource sharing is achieved; and the system has a fault-tolerant capability, so that when some computers have faults, the system can still continue to operate and provide services to the outside.