Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss prevention and global food security. However, precise and continuous spectral signatures, important for large-area cro...

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Bibliographic Details
Main Authors: Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
DLR earth sensing imaging spectrometer (DESIS)
hyperspectral remote sensing
random forest (RF)
spectral library
yield prediction
Online Access:https://ieeexplore.ieee.org/document/10032208/
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