Measurement of sown area of safflower based on PCA and texture features classification and remote sensing imagery.
- Author:
Ren-Hua NA
1
;
Jiang-Hua ZHENG
2
;
Bao-Lin GUO
3
;
Ba-Ti SEN
2
;
Min-Hui SHI
3
;
Zhi-Qun SUN
2
;
Xiao-Guang JIA
3
;
Xiao-Jin LI
3
Author Information
1. School of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China. 499361608@qq.com
2. School of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China.
3. Xinjiang Chinese and Minority Nationality Medicine Research Institute, Urumqi 830002, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Carthamus tinctorius;
chemistry;
growth & development;
Image Processing, Computer-Assisted;
Pattern Recognition, Automated;
Principal Component Analysis;
methods;
Remote Sensing Technology;
methods
- From:
China Journal of Chinese Materia Medica
2013;38(21):3681-3686
- CountryChina
- Language:Chinese
-
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.
