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170results about How to "Accurate area" patented technology

Image processing

There is described an image processing method in which a scene is repeatedly imaged to form a series of input images. For at least a subset of the input images, a colour calibration procedure is conducted which populates a foreground colour histogram with the frequency of occurrence of colour values in a stencil area of the input image, and populates a background colour histogram with the frequency of occurrence of colour values outside of the stencil portion of the input image. For at least a subset of the input images, a colour replacement procedure is conducted which updates the stencil area based on a determination, from the colour values of pixels within the input image, of likelihood values representing the likelihood of pixels belonging to an image area of interest, the likelihood value for each colour value being determined from a combination of the foreground and background colour histograms, replaces the original colour values of pixels within the updated stencil area of the input image with replacement colour values, and displays the image on which colour replacement processing has been conducted. In this way, a stencil area is determined based on foreground/background histogramming, and used both to define an area to which colour replacement processing is to be conducted, and also an area for use in further populating the colour histograms to calibrate the colour replacement processing.
Owner:HOLITION

Vegetation classification method based on machine learning algorithm and multi-source remote sensing data fusion

The invention relates to the field of ecological environment monitoring, and discloses a vegetation classification method based on a machine learning algorithm and multi-source remote sensing data fusion, which is used for efficiently realizing identification and classification of vegetation types in a target area. The method comprises the following steps: acquiring a low-altitude remote sensing image of terrestrial plants in a sample area by using an unmanned aerial vehicle, and acquiring a digital orthoimage and a digital surface model of the sample area based on the low-altitude remote sensing image; extracting elevation information of the digital surface model; acquiring an SAR image of a sample region corresponding to the aerial photography time of the unmanned aerial vehicle by utilizing satellite remote sensing; carrying out wave band and image fusion on the digital orthoimage, the elevation information and the SAR image; performing inversion model training and inversion model precision evaluation on the fused image through sample area actual measurement data and a machine learning algorithm to obtain an inversion model meeting requirements; and finally, classifying terrestrial plants in the target area based on the inversion model. The method is suitable for terrestrial plant ecological environment monitoring.
Owner:CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE
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