Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Random Forests

Sortuj według:

Ogranicz wyniki do:

Automatic detection of dominant crop types in Poland based on satellite images

Pluto-Kossakowska Joanna, Pilarska Magdalena, Bartkowiak Paulina

Artificial Satellites : Journal of Planetary Geodesy

2020

Vol. 55, No. 4

185--208

The assumption of the European Union Common Agricultural Policy is to maintain good agricultural practices for sustainability in the environment. A number of requirements are imposed on farmers, including the maintenance of permanent grassland, fallow land or crop diversification. To meet these requirements, the European Union guarantees subsidies, but at the same time fields must be monitored focusing on crop identification. The limitation of field inspection and substituting it with crop recognition using satellite images could increase the effectiveness of this procedure. The application of satellite imagery in automatic detection and identification of dominant crops over a large area seems to be technically and economically sound. The paper discusses the concept and the results of automatic classification based on a Random Forests classifier performed on multitemporal images of Sentinel-2 and Landsat-8. A test site was established in a complex agricultural structure with long and narrow parcels in the south-eastern part of Poland. Time-series images acquired during the growing season 2016 were used for multispectral classification in different configurations: for Sentinel-2 and Landsat-8 separately and for both sensors integrated. Different Random Forests approaches and post-processing methods were examined based on independent data from farmers’ declarations records, reaching the best accuracy of over 90% for crops like winter or spring cereals. Overall accuracy of the classification ranged from 72% to 91% depending on the classification variant. The elaborated scheme is novel in the context of Polish complex agricultural structure and smallholders.

Data mining with Random Forests as a methodology for biomedical signal classification

Proniewska K.

Bio-Algorithms and Med-Systems

2016

Vol. 12, no. 2

89--92

As the contribution of specific parameters is not known and significant intersubject variability is expected, a decision system allowing adaptation for subject and environment conditions has to be designed to evaluate biomedical signal classification. A decision support system has to be trained in its desirable functionality prior to being used for patient monitoring evaluation. This paper describes a decision system based on data mining with Random Forests, allowing the adaptation for subject and environment conditions. This methodology may lead to specific system scoring by an artificial intelligence-supported patient monitoring evaluation system, which may help find a way of making decisions concerning future treatment and have influence on the quality of patients’ life.