[Measurement of sown area of safflower based on PCA and texture features classification and remote sensing imagery].
Zhongguo Zhong Yao Za Zhi
; 38(21): 3681-6, 2013 Nov.
Article
em Zh
| MEDLINE
| ID: mdl-24494554
ABSTRACT
To improve accuracy of estimation in planted safflower acreage,we selected agricultural area in Yumin County, Xinjiang as the study area. There safflower was concentrated planted. Supervised classification based on Principal Component Analysis (PCA) and texture feature were used to obtain the safflower acreage from image captured by ZY-3. The classification result was compared with only spectral feature and spectral feature with texture feature. The research result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification. The overall accuracy is 87.519 1%, which increases by 7.117 2% compared with single data source classification. Therefore, the classification method based on PCA and texture features can be adapted to RS image classification and estimate the acreage of safflower. This study provides a feasible solution for estimation of planted safflower acreage by image captured by ZY-3 satellite.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Componente Principal
/
Carthamus tinctorius
/
Tecnologia de Sensoriamento Remoto
Tipo de estudo:
Evaluation_studies
Idioma:
Zh
Revista:
Zhongguo Zhong Yao Za Zhi
Assunto da revista:
FARMACOLOGIA
/
TERAPIAS COMPLEMENTARES
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
China