Advantage of multispectral imaging with sub-centimeter resolution in precision agriculture: generalization of training for supervised classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F17%3A00464860" target="_blank" >RIV/86652079:_____/17:00464860 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43210/17:43909946

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11119-016-9478-1" target="_blank" >http://dx.doi.org/10.1007/s11119-016-9478-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11119-016-9478-1" target="_blank" >10.1007/s11119-016-9478-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advantage of multispectral imaging with sub-centimeter resolution in precision agriculture: generalization of training for supervised classification

Popis výsledku v původním jazyce

Nowadays it is known how to resolve many questions through satellite imagery such as Landsat 8 and the like, both from the theoretical point of view, i.e. research, as well as from the practical standpoint, e.g. commercial applications. This study evaluated the possibility of generalizing the training for supervised classification of multispectral images with sub-centimeter resolution. Images were taken under uncontrolled conditions of lighting and sun-target-sensor geometry and in the presence of normal interference in the agricultural environment. The images were obtained by the DuncanTech MS3100 camera (Auburn, CA, USA), a multispectral camera (green, red and near infra-red) mounted on a mobile ground platform and transformed into reflectance. For each element present (leaves, stems, spikes, soil, shadows, spectral references and sampling implements), a representative area was delimited in each image. These regions of interest were used, first, to quantify the separability of the classes. The next step was to define groups for cross-validation within these regions of interest, ten-folds were defined randomly with the constraint of a uniform distribution of classes. These folds were used in training and evaluation of the supervised classification using spectral angle mapper, maximum likelihood and decision trees. Spectral angle mapper correctly classified 49.2 % of cases, the maximum likelihood achieved a success rate of 86.8 % and the decision tree correctly classified 99.5 % of the spectral signatures. These results prove that multispectral images taken under uncontrolled conditions can be successfully classified by a generalized model that takes advantage of the higher spatial resolution. This opens a new line in which those pixels that do not correspond to vegetation, which bias the estimates of the crop parameters and complicate the recognition of objects, could be automatically masked.

Název v anglickém jazyce

Advantage of multispectral imaging with sub-centimeter resolution in precision agriculture: generalization of training for supervised classification

Popis výsledku anglicky

Nowadays it is known how to resolve many questions through satellite imagery such as Landsat 8 and the like, both from the theoretical point of view, i.e. research, as well as from the practical standpoint, e.g. commercial applications. This study evaluated the possibility of generalizing the training for supervised classification of multispectral images with sub-centimeter resolution. Images were taken under uncontrolled conditions of lighting and sun-target-sensor geometry and in the presence of normal interference in the agricultural environment. The images were obtained by the DuncanTech MS3100 camera (Auburn, CA, USA), a multispectral camera (green, red and near infra-red) mounted on a mobile ground platform and transformed into reflectance. For each element present (leaves, stems, spikes, soil, shadows, spectral references and sampling implements), a representative area was delimited in each image. These regions of interest were used, first, to quantify the separability of the classes. The next step was to define groups for cross-validation within these regions of interest, ten-folds were defined randomly with the constraint of a uniform distribution of classes. These folds were used in training and evaluation of the supervised classification using spectral angle mapper, maximum likelihood and decision trees. Spectral angle mapper correctly classified 49.2 % of cases, the maximum likelihood achieved a success rate of 86.8 % and the decision tree correctly classified 99.5 % of the spectral signatures. These results prove that multispectral images taken under uncontrolled conditions can be successfully classified by a generalized model that takes advantage of the higher spatial resolution. This opens a new line in which those pixels that do not correspond to vegetation, which bias the estimates of the crop parameters and complicate the recognition of objects, could be automatically masked.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PRECISION AGRICULTURE

ISSN

1385-2256

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

615-634

Kód UT WoS článku

000404711800009

EID výsledku v databázi Scopus

2-s2.0-84992752916