APPARATUS AND METHOD FOR SINGLE LOOK MAIN LOBE AND SIDELOBE DISCRIMINATION IN SPECTRAL DOMAIN IMAGES

    公开(公告)号:US20180053070A1

    公开(公告)日:2018-02-22

    申请号:US15677376

    申请日:2017-08-15

    申请人: RFNAV, Inc

    IPC分类号: G06K9/62 G06K9/52

    摘要: A system performs operations including receiving multi-dimensional single-look data from a sensor, applying multi-dimensional complex weighting functions including apodizations from among a general class of such functions to the complex data, so as to induce nonlinear variations in the amplitude and phase of the multi-dimensional spectral image responses, forming a number of features per voxel across a number of multi-dimensional spectral image responses, and using a multi-dimensional non-parametric classifier to form features to discriminate main lobe from sidelobe imaged voxels with the weighting function applied to received data. The operations include identifying each voxel by processing a set of transforms from the multi-dimensional complex weighting functions and outputting a multi-dimensional main lobe binary image, representing main lobe versus sidelobe locations.

    Apparatus and method for single look main lobe and sidelobe discrimination in spectral domain images

    公开(公告)号:US09953244B2

    公开(公告)日:2018-04-24

    申请号:US15677376

    申请日:2017-08-15

    申请人: RFNAV, Inc

    IPC分类号: G06K9/62 G06K9/52

    摘要: A system performs operations including receiving multi-dimensional single-look data from a sensor, applying multi-dimensional complex weighting functions including apodizations from among a general class of such functions to the complex data, so as to induce nonlinear variations in the amplitude and phase of the multi-dimensional spectral image responses, forming a number of features per voxel across a number of multi-dimensional spectral image responses, and using a multi-dimensional non-parametric classifier to form features to discriminate main lobe from sidelobe imaged voxels with the weighting function applied to received data. The operations include identifying each voxel by processing a set of transforms from the multi-dimensional complex weighting functions and outputting a multi-dimensional main lobe binary image, representing main lobe versus sidelobe locations.