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UID:calendarize-machine-learning-driven-analysis-of-hyperspectral-imaging-hsi-data
DTSTAMP:20250303T135148Z

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DTEND:20250307T153000Z
    
SUMMARY:Machine Learning-Driven Analysis of Hyperspectral Imaging (HSI) Data
DESCRIPTION:LU EZTF Astronomijas institūta un LU NZP FOTONIKA-LV zinātniskais seminārs “Machine Learning-Driven Analysis of Hyperspectral Imaging (HSI) Data”.\nSeminārs notiek attālināti Zoom.\nWe invite you to the remote seminar: Fazel Hesar Fatemeh “Machine Learning-Driven Analysis of Hyperspectral Imaging (HSI) Data"\nAbstract:  Hyperspectral imaging (HSI) has emerged as a tool for capturing detailed spectral and spatial information across applications\, including agriculture\, environmental monitoring\, and medical diagnostics. However\, the high-dimensional and complex nature of HSI data poses significant challenges for traditional analysis methods. Machine learning (ML) offers a transformative approach to extracting meaningful insights from this data\, enabling tasks such as classification\, anomaly detection\, and material identification with unprecedented accuracy. This presentation explores the integration of ML techniques—such as supervised learning\, unsupervised learning\, and deep learning—with hyperspectral imaging data. We will discuss preprocessing strategies\, feature extraction\, model selection\, and real-world case studies\, highlighting the potential and limitations of these approaches.\nThe seminar is realized within ERDF project No. 1.1.1.5/2/24/A/004 "ERA Chair in Astrophysics\, Instrumentation\, Ground Segment Technologies and Space Photonics at the University of Latvia".\nZoom link for access:\nhttps://zoom.us/j/97419913654?pwd=QjVUY21ja0FXRStYZ3ZYZlptSkFJQT09Meeting ID: 974 1991 3654\,  Passcode: 420982
X-ALT-DESC;FMTTYPE=text/html:<p>LU EZTF Astronomijas institūta un LU NZP FOTONIKA-LV zinātniskais seminārs “Machine Learning-Driven Analysis of Hyperspectral Imaging (HSI) Data”.</p>\n<p>Seminārs notiek attālināti Zoom.</p>\n<p>We invite you to the remote seminar: Fazel Hesar Fatemeh “Machine Learning-Driven Analysis of Hyperspectral Imaging (HSI) Data"</p>\n<p>Abstract: &nbsp\;Hyperspectral imaging (HSI) has emerged as a tool for capturing detailed spectral and spatial information across applications\, including agriculture\, environmental monitoring\, and medical diagnostics. However\, the high-dimensional and complex nature of HSI data poses significant challenges for traditional analysis methods. Machine learning (ML) offers a transformative approach to extracting meaningful insights from this data\, enabling tasks such as classification\, anomaly detection\, and material identification with unprecedented accuracy. This presentation explores the integration of ML techniques—such as supervised learning\, unsupervised learning\, and deep learning—with hyperspectral imaging data. We will discuss preprocessing strategies\, feature extraction\, model selection\, and real-world case studies\, highlighting the potential and limitations of these approaches.</p>\n<p>The seminar is realized within ERDF project No. 1.1.1.5/2/24/A/004 "ERA Chair in Astrophysics\, Instrumentation\, Ground Segment Technologies and Space Photonics at the University of Latvia".</p>\n<p>Zoom link for access:</p>\n<p>https://zoom.us/j/97419913654?pwd=QjVUY21ja0FXRStYZ3ZYZlptSkFJQT09<br />Meeting ID: 974 1991 3654\, &nbsp\;Passcode: 420982</p>
LOCATION:Zoom. (https://zoom.us/j/97419913654?pwd=QjVUY21ja0FXRStYZ3ZYZlptSkFJQT09)
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