HypsLiDNet: 3-D–2-D CNN Model and Spatial–Spectral Morphological Attention for Crop Classification With DESIS and LiDAR Data

The advent of cloud computing and advanced processing technologies has elevated deep learning (DL) as a leading method for hyperspectral imaging (HSI) classification. Classifying crops accurately is vital for generating precise agricultural data to support informed decision-making. This study introd...

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Detalhes bibliográficos

Principais autores:	Nizom Farmonov, M. Esmaeili, Dariush Abbasi-Moghadam, Alireza Sharifi, Khilola Amankulova, Laszlo Mucsi
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2024-01-01
coleção:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Attention mechanism convolution neural network (CNN) DLR earth sensing imaging spectrometer (DESIS) hyperspectral image (HSI) image classification light detection and ranging (LiDAR)
Acesso em linha:	https://ieeexplore.ieee.org/document/10571369/
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