A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification

<p>Hyperspectral imaging (HSI) plays a pivotal role in remote sensing, enabling precise material identification through spectral data across many bands. Despite its advantages, challenges like high dimensionality, spectral mixing, and limited labelled data hinder its classification accuracy. T...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mohamed Sultan Mohamed Ali (17317003) (author)
مؤلفون آخرون:	Md Sakib Bin Islam (22804121) (author), Molla E. Majid (21323921) (author), Saad Bin Abul Kashem (17773188) (author), Amith Khandakar (14151981) (author), Muhammad E.H. Chowdhury (17151154) (author)
منشور في:	2025
الموضوعات:	Agricultural, veterinary and food sciences Agricultural biotechnology Environmental sciences Environmental management Information and computing sciences Artificial intelligence Data management and data science Hyperspectral imaging (HSI) Remote sensing 3D convolutional neural networks (CNNs) Long short-term memory (LSTM) Mixed pixels
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A hybrid 3D CNN-LSTM model with soft spatial attention mechanism for accurate hyperspectral image classification

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