Comparison of UAV and SAR performance for Crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of West Africa-Reference-Cited by-同舟云学术

Comparison of UAV and SAR performance for Crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of West Africa

Published:2022-06-03 Issue:11 Volume:43 Page:4259-4286
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Duke Ojo Patrick¹,Alabi Tunrayo²^ORCID,Neeti Neeti¹,Adewopo Julius³

Affiliation:

1. Department of Natural and Applied Sciences, TERI School of Advanced Studies, New Delhi, India

2. Geospatial Laboratory, International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria

3. Geospatial Laboratory, International Institute of Tropical Agriculture (IITA)- Rwanda, Kacyiru, Kigali, Rwanda

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2109444

Reference57 articles.

1. Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

2. Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa

3. Statistics Notes: Diagnostic tests 1: sensitivity and specificity

4. Statistics Notes: Diagnostic tests 2: predictive values

5. Evaluation of random forest method for agricultural crop classification

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