首页|Study Findings from Dr. Vishwanath Karad MIT World Peace University Advance Know ledge in Support Vector Machines (Automatic Speckle Noise Reduction in SAR Image ry Using Optimized Kernel Support Vector Machine)
Study Findings from Dr. Vishwanath Karad MIT World Peace University Advance Know ledge in Support Vector Machines (Automatic Speckle Noise Reduction in SAR Image ry Using Optimized Kernel Support Vector Machine)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on . According to news originating from Maharashtra,India,by NewsRx correspondents,research stated,"Speckle noise (SN) is one of the major types of noise that fr equently occurs in different coherent imaging systems such as medical imaging,S ynthetic Aperture Radar (SAR) and active Radar images. SAR is a powerful imaging technology that generates fine-resolution images and monitors the earth's surfa ce in order to identify its physical properties." The news editors obtained a quote from the research from Dr. Vishwanath Karad MI T World Peace University: "The satellite images captured by SAR are mainly affec ted by SN,which reduces the quality of images and complicates the image represe ntation. Therefore,removing SN from SAR images is one of the major challenges a nd needs significant attention. The proposed study introduces an optimal Machine Learning (ML) classifier named Kernel Support Vector Machine-Improved Aquila Op timization (KSVMIAO) for reducing SN in SAR images. This study uses a two-step process called filtering and enhanced despeckling to minimize the consequence of speckle suppression. In the first step,different imaging filters,namely Impro ved Lee Filter (ILF),Improved Frost Filter (IFF),Improved Kuan Filter (IKF) an d Improved Boxcar Filter (IBF),are utilized to remove the SN in SAR images. Nex t,the denoised image is fed to the second stage,which makes use of an optimize d KSVM-IAO classifier to obtain an enhanced despeckle image."
Dr. Vishwanath Karad MIT World Peace Uni versityMaharashtraIndiaAsiaEmerging TechnologiesMachine LearningSupp ort Vector MachinesVector Machines
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