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利用 NOAA/AVHRR资料进行省级水稻遥感长势监测与估产是以高精度配准为前提的 ,因此首先论述了在利用 TBUS报进行系统纠正的基础上 ,还必须进行几何精纠正的必要性及其方法的选择 ;提出综合利用 ARC/INFO和 ENVI的功能建立几何精纠正的标准空间 ,通过各种变换进行图像增强处理 ,仔细选取地面控制点 ;利用 5 9次浙江省范围的 NOAA/AVHRR数据计算出不同方法中的误差 ,首次通过方差分析和多重比较进行统计检验 .结果表明 :RST,二元一次多项式拟合法和二元二次多项式拟合法之间的 RMS差异达到极显著水平 ,选定二元二次多项式拟合法作为几何精纠正方法 ,灰度值重采样采用三次卷积插值法 ,可作为采用 NOAA/AVHRR资料开展省级水稻面积遥感估算、长势监测与估产的基础
The use of NOAA / AVHRR data for provincial rice remote sensing growth monitoring and assessment is based on the premise of high-precision registration, and therefore first discusses the use of TBUS reported on the basis of system correction, but also the need for geometric correction and its The paper proposes a method to synthetically utilize the functions of ARC / INFO and ENVI to establish the geometric correction standard space, and enhances the images through various transformations and carefully selects the ground control points. Using the NOAA / AVHRR data of 59 provinces in Zhejiang Province The error of different methods was tested for the first time by means of analysis of variance and multiple comparisons. The results showed that the difference of RMS between RST, bivariate first-order polynomial fitting and bivariate second-order polynomial fitting reached extremely significant level, Element quadratic polynomial fitting method As the geometric rectification method, the gray value resampling method uses cubic convolution interpolation method, which can be used as the basis for the provincial rice area remote sensing estimation, growth monitoring and yield estimation using NOAA / AVHRR data