基于PCA和纹理特征的红花种植面积遥感估算(5)
2、Xinjiang Education Ministry Key Lab of City Inteligenlizing and Environment Modeling， Urumqi 830046， China
3、 Xinjiang Chinese and Minority Nationality M edicine Research Institute， Urumqi 830002， China)
[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.
[Key words] safflower； acreage； principal component analysis (PCA)； texture features
doi：10.4268/cjcmm20132116
[责任编辑 吕冬梅], 百拇医药(娜仁花 郑江华 郭宝林 森巴提 石明辉 孙志群 贾晓光 李晓瑾)
3、 Xinjiang Chinese and Minority Nationality M edicine Research Institute， Urumqi 830002， China)
[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.
[Key words] safflower； acreage； principal component analysis (PCA)； texture features
doi：10.4268/cjcmm20132116
[责任编辑 吕冬梅], 百拇医药(娜仁花 郑江华 郭宝林 森巴提 石明辉 孙志群 贾晓光 李晓瑾)