Multispectral Texture Classification in Agriculture

2023

Texture classification plays an important role in different domains including agricultural applications, where unmanned vehicles such as drones equipped with multispectral sensors are gaining more attention. Hence, a solution which does not require substantial computational resources is desired for real-time monitoring. In this contribution, we propose an efficient and interpretable Generalized Matrix Learning Vector Quantization (GMLVQ) based framework to classify multispectral images. We demonstrate the performance of different model designs and compare them to other benchmarks for the classification of a soil data set. Our framework yields comparable accuracy while providing explainable results.

@inproceedings{Shumska2023,
title = {Multispectral Texture Classification in Agriculture},
author = {Mariya Shumska and Kerstin Bunte},
month = {4-6 October},
organization = {},
editor = {Michel Verleysen},
booktitle = {Proc. of the  31th "European Symposium on Artificial Neural Networks (ESANN)},
year = {2023},
publisher = {i6doc.com},
address = {Bruges (Belgium) and online event},
pages = {},
abstract = {Texture classification plays an important role in different domains including agricultural applications, where unmanned vehicles such as drones equipped with multispectral sensors are gaining more attention. Hence, a solution which does not require substantial computational resources is desired for real-time monitoring. In this contribution, we propose an efficient and interpretable Generalized Matrix Learning Vector Quantization (GMLVQ) based framework to classify multispectral images. We demonstrate the performance of different model designs and compare them to other benchmarks for the classification of a soil data set. Our framework yields comparable accuracy while providing explainable results.},
doi = {},
url = {},
isbn = {},
}