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4129 results about "Very high resolution" patented technology

Unmanned aerial vehicle multi-overlapped-remote-sensing-image method for extracting building contour line

The invention discloses an unmanned aerial vehicle multi-overlapped-remote-sensing-image method for extracting a building contour line. The unmanned aerial vehicle multi-overlapped-remote-sensing-image method includes the steps that three-dimensional point cloud is generated with an aerial-triangulation and dense-matching combined method and filtered, and a building is detected from the point cloud; after the walls of the detected building are canceled, the general contour of the building is extracted from building top face information; the general contour of the building serves as a buffering area to be overlapped on spliced images and serves as shape prior information, evolution is carried out in the buffering area with a level set algorithm, and finally an accurate contour of the building is obtained. By means of the unmanned aerial vehicle multi-overlapped-remote-sensing-image method, as point cloud three-dimensional information generated by the multiple overlapped images is sufficiently used, and meanwhile the high-accuracy geometrical information of the high-resolution remote sensing images is used in a combined mode, the building contour extracting accuracy is remarkably improved, and the complexity of the method is lowered.
Owner:WUHAN UNIV

Automatic collecting method of high-resolution satellite remote sensing traffic flow information

The invention discloses an automatic collecting method of high-resolution satellite remote sensing traffic flow information. The automatic collecting method comprises the following steps of: A. pretreatment: registration of panchromatic images and a vector road network as well as panchromatic and multi-spectral images, road region division and double edge filtering strengthen; B. acquiring a vehicle sample characteristic value by visually judging a road region image obtained in the step one, and establishing a vehicle remote sensing image feature library; C. carrying out rough neural network vehicle extraction and fine facing objective vehicle extraction on the panchromatic images obtained in the step two; D. by utilizing a matching method relative to an image frequency domain, searching the vehicle position in fine extraction in the step three in multi-spectral images and carrying out matching; E. calculating a displacement amount of a same vehicle in the panchromatic and multi-spectral images according to the corresponding vehicle position obtained in the step three and the step four, thus estimating traffic flow parameter information; and F. verifying the traffic flow parameter information through precision evaluation. With the adoption of the method, static and dynamic traffic flow information in a large range series can be automatically and rapidly collected, the efficiency is high, and the method is simple and practicable.
Owner:REMOTE SENSING APPLIED INST CHINESE ACAD OF SCI

Remote sensing image target detection method based on new frame regression loss function

The invention provides a remote sensing image target detection method based on a new frame regression loss function. The method comprises the following steps: training a candidate region generation network through employing a high-resolution remote sensing image as a training sample, and enabling the frame regression loss function of the candidate region generation network to employ the new loss function; obtaining a candidate target frame as a target initial position training region detection network through the trained candidate region generation network, wherein a new frame regression lossfunction is adopted as a frame regression loss function of the region detection network; alternately training a candidate region generation network and a region detection network; and sharing backbonenetworks of the candidate region generation network and the region detection network, combining the trained candidate region generation network and the region detection network to construct a detection model, and obtaining the position and the category of the interested target of the to-be-detected high-resolution remote sensing image. By improving the frame regression loss function of target detection, the target detection precision of the high-resolution remote sensing image can be effectively improved.
Owner:ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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