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

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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Bibliographic Details
Main Author: Mohamed Sultan Mohamed Ali (17317003) (author)
Other Authors: 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)
Published: 2025
Subjects:
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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