Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

Published:2023 Issue: Volume:16 Page:1576-1588
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Farmonov Nizom¹^ORCID,Amankulova Khilola¹^ORCID,Szatmari Jozsef¹^ORCID,Sharifi Alireza²^ORCID,Abbasi-Moghadam Dariush³^ORCID,Mirhoseini Nejad Seyed Mahdi³^ORCID,Mucsi Laszlo¹^ORCID

Affiliation:

1. Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Szeged, Hungary

2. Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teachers Training University, Tehran, Iran

3. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Funder

University of Szeged Open Access Fund

Ministry of Innovation and Technology of Hungary

Nemzeti Kutatási, Fejlesztési és Innovaciós Alap

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10032208.pdf?arnumber=10032208

Reference56 articles.

1. Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

2. Remote sensing satellite’s attitude control system: rapid performance sizing for passive scan imaging mode

3. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data

4. Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

5. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

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