Version 1
: Received: 19 February 2023 / Approved: 20 February 2023 / Online: 20 February 2023 (08:31:21 CET)
How to cite:
Hejmanowska, B.J.; Kramarczyk, P.S. Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland. Preprints2023, 2023020331. https://doi.org/10.20944/preprints202302.0331.v1
Hejmanowska, B.J.; Kramarczyk, P.S. Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland. Preprints 2023, 2023020331. https://doi.org/10.20944/preprints202302.0331.v1
Hejmanowska, B.J.; Kramarczyk, P.S. Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland. Preprints2023, 2023020331. https://doi.org/10.20944/preprints202302.0331.v1
APA Style
Hejmanowska, B.J., & Kramarczyk, P.S. (2023). Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland. Preprints. https://doi.org/10.20944/preprints202302.0331.v1
Chicago/Turabian Style
Hejmanowska, B.J. and Piotr Szymon Kramarczyk. 2023 "Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland" Preprints. https://doi.org/10.20944/preprints202302.0331.v1
Abstract
This study aimed to investigate the possibility of using one-shot hyperspectral airborne images to recognize crops for an area with many small plots. The results showed that unsupervised clustering methods could classify crops with an accuracy of 80%, which improved to 90% when restricted to only grain crops, using a single airborne hyperspectral recording. However, additional layers such as NDVI, DTM, slope, and aspect did not improve classification accuracy. For comparison, the accuracy of clustering time series Sentinel-2 images with NDVI layers and DTM-derived data yielded an accuracy of: 74% ,Sentinel-2 time series 68% and single one registration before harvest - 39%. The results of the random forest classification were slightly less accurate due to a lack of sufficient reference data. However, it is challenging to verify the reported accuracy of crop recognition in the literature above 90% due to differences in analysis methodologies, reference data selection, pixel/object approaches, metric choice, and calculation formulas used.
Keywords
n/a; airborne hyperspectral images, Sentinel-2, k-means, random forest, crop recognition
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
