TERRAIN ESTIMATION USING LOW RESOLUTION IMAGERY

    公开(公告)号:US20230386200A1

    公开(公告)日:2023-11-30

    申请号:US17804195

    申请日:2022-05-26

    CPC classification number: G06V20/194 G06V10/60 G06V10/774 G06V20/188

    Abstract: A computing system measures terrain coverage by: obtaining sample image data representing a multispectral image of a geographic region at a sample resolution; generating, based on the sample image data, an index array of pixels for a subject terrain in which each pixel has an index value that represents a predefined relationship between a first wavelength reflectance and a second wavelength reflectance; providing the index array to a trained calibration model to generate an estimated value based on the index array, the estimated value representing an estimated amount of terrain coverage within the geographic region for the subject terrain; and outputting the estimated value for the subject terrain. The trained calibration model may be trained based on training data representing one or more reference images of one or more training geographic regions containing the subject terrain at a higher resolution than the sample resolution.

    INTERSATELLITE IMAGING DATA TRANSFER
    3.
    发明公开

    公开(公告)号:US20240364418A1

    公开(公告)日:2024-10-31

    申请号:US18306856

    申请日:2023-04-25

    CPC classification number: H04B7/18521 H04B7/18584 H04B7/18586

    Abstract: A computing device including a processor configured to receive satellite status data from satellites included in a satellite constellation. The processor is further configured to determine a link topology of the satellites. Based at least in part on the satellite status data and the link topology, the processor is further configured to identify a first satellite constellation subset including one or more selected satellite pairs. Identifying the one or more selected satellite pairs includes computing respective link utility values associated with a plurality of candidate pairs of satellites included in the satellite constellation based at least in part on the satellite status data and the link topology. The one or more selected satellite pairs are selected based at least in part on the link utility values. The processor is further configured to transmit, to the satellites included in the first satellite constellation subset, instructions to perform intersatellite imaging data transfer.

    RECOVERING OCCLUDED IMAGE DATA USING MACHINE LEARNING

    公开(公告)号:US20210133936A1

    公开(公告)日:2021-05-06

    申请号:US16786257

    申请日:2020-02-10

    Abstract: Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

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